{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "423fbe04-d527-4946-8023-a9c7cf68a27e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Attaching SeuratObject\n",
      "\n",
      "Registered S3 method overwritten by 'cli':\n",
      "  method     from         \n",
      "  print.boxx spatstat.geom\n",
      "\n",
      "Warning message in if (is.na(desc)) {:\n",
      "“the condition has length > 1 and only the first element will be used”\n",
      "Warning message in if (is.na(desc)) {:\n",
      "“the condition has length > 1 and only the first element will be used”\n",
      "Warning message in if (is.na(desc)) {:\n",
      "“the condition has length > 1 and only the first element will be used”\n",
      "Warning message in if (is.na(desc)) {:\n",
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      "Warning message in if (is.na(desc)) {:\n",
      "“the condition has length > 1 and only the first element will be used”\n",
      "Warning message in if (is.na(desc)) {:\n",
      "“the condition has length > 1 and only the first element will be used”\n",
      "Warning message in if (is.na(desc)) {:\n",
      "“the condition has length > 1 and only the first element will be used”\n",
      "Warning message in if (is.na(desc)) {:\n",
      "“the condition has length > 1 and only the first element will be used”\n",
      "Warning message in if (is.na(desc)) {:\n",
      "“the condition has length > 1 and only the first element will be used”\n",
      "Warning message in if (is.na(desc)) {:\n",
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      "Warning message in if (is.na(desc)) {:\n",
      "“the condition has length > 1 and only the first element will be used”\n",
      "Warning message in if (is.na(desc)) {:\n",
      "“the condition has length > 1 and only the first element will be used”\n",
      "Registered S3 method overwritten by 'SeuratDisk':\n",
      "  method            from  \n",
      "  as.sparse.H5Group Seurat\n",
      "\n",
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘matrixStats’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:dplyr’:\n",
      "\n",
      "    count\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘MatrixGenerics’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:matrixStats’:\n",
      "\n",
      "    colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,\n",
      "    colCounts, colCummaxs, colCummins, colCumprods, colCumsums,\n",
      "    colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,\n",
      "    colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,\n",
      "    colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,\n",
      "    colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,\n",
      "    colWeightedMeans, colWeightedMedians, colWeightedSds,\n",
      "    colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,\n",
      "    rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,\n",
      "    rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,\n",
      "    rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,\n",
      "    rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,\n",
      "    rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,\n",
      "    rowWeightedMads, rowWeightedMeans, rowWeightedMedians,\n",
      "    rowWeightedSds, rowWeightedVars\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘BiocGenerics’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:parallel’:\n",
      "\n",
      "    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,\n",
      "    clusterExport, clusterMap, parApply, parCapply, parLapply,\n",
      "    parLapplyLB, parRapply, parSapply, parSapplyLB\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:dplyr’:\n",
      "\n",
      "    combine, intersect, setdiff, union\n",
      "\n",
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      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    IQR, mad, sd, var, xtabs\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    anyDuplicated, append, as.data.frame, basename, cbind, colnames,\n",
      "    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,\n",
      "    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,\n",
      "    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,\n",
      "    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,\n",
      "    union, unique, unsplit, which.max, which.min\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘S4Vectors’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:dplyr’:\n",
      "\n",
      "    first, rename\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:base’:\n",
      "\n",
      "    expand.grid\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘IRanges’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:dplyr’:\n",
      "\n",
      "    collapse, desc, slice\n",
      "\n",
      "\n",
      "Welcome to Bioconductor\n",
      "\n",
      "    Vignettes contain introductory material; view with\n",
      "    'browseVignettes()'. To cite Bioconductor, see\n",
      "    'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘Biobase’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:MatrixGenerics’:\n",
      "\n",
      "    rowMedians\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:matrixStats’:\n",
      "\n",
      "    anyMissing, rowMedians\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘SummarizedExperiment’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:SeuratObject’:\n",
      "\n",
      "    Assays\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:Seurat’:\n",
      "\n",
      "    Assays\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘data.table’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:SummarizedExperiment’:\n",
      "\n",
      "    shift\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:GenomicRanges’:\n",
      "\n",
      "    shift\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:IRanges’:\n",
      "\n",
      "    shift\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:S4Vectors’:\n",
      "\n",
      "    first, second\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:dplyr’:\n",
      "\n",
      "    between, first, last\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘Matrix’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:S4Vectors’:\n",
      "\n",
      "    expand\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(Seurat, quietly = TRUE)\n",
    "library(SeuratData, quietly = TRUE)\n",
    "library(SeuratDisk, quietly = TRUE)\n",
    "library(dplyr, quietly = TRUE)\n",
    "library(ArchR, quietly = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "873f3d3d-cec5-4e22-8ffa-7795da366d8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Successfully loaded ArchRProject!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## load metadata\n",
    "proj <- loadArchRProject(path = \"../snATAC/DataIntegration/data/VisiumHeart\", showLogo = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8a0a67ae-39ef-42be-bd8b-dec93c8940ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ArchR logging to : ArchRLogs/ArchR-getMatrixFromProject-3b13bb72e4e1e9-Date-2021-11-04_Time-16-22-53.log\n",
      "If there is an issue, please report to github with logFile!\n",
      "\n",
      "2021-11-04 16:28:53 : Organizing colData, 6.011 mins elapsed.\n",
      "\n",
      "2021-11-04 16:28:54 : Organizing rowData, 6.017 mins elapsed.\n",
      "\n",
      "2021-11-04 16:28:54 : Organizing rowRanges, 6.017 mins elapsed.\n",
      "\n",
      "2021-11-04 16:28:54 : Organizing Assays (1 of 1), 6.017 mins elapsed.\n",
      "\n",
      "2021-11-04 16:31:38 : Constructing SummarizedExperiment, 8.746 mins elapsed.\n",
      "\n",
      "2021-11-04 16:31:39 : Finished Matrix Creation, 8.765 mins elapsed.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## get a Seurat object for ATAC-seq\n",
    "geneMatrix <- getMatrixFromProject(proj, useMatrix = \"GeneScoreMatrix\")\n",
    "GeneScoreMatrix <- geneMatrix@assays@data$GeneScoreMatrix\n",
    "rownames(GeneScoreMatrix) <- geneMatrix@elementMetadata$name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "16e04a71-eff6-4dd7-b9d8-a9a0d62100c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load Seurat object\n",
    "obj <- readRDS(\"../snATAC/DataIntegration/data/VisiumHeart/snATAC.annotated.Rds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bcd82b8b-49bd-4436-893f-46b4de1f4ba5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 39</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>orig.ident</th><th scope=col>nCount_peaks</th><th scope=col>nFeature_peaks</th><th scope=col>Sample</th><th scope=col>TSSEnrichment</th><th scope=col>ReadsInTSS</th><th scope=col>ReadsInPromoter</th><th scope=col>ReadsInBlacklist</th><th scope=col>PromoterRatio</th><th scope=col>PassQC</th><th scope=col>⋯</th><th scope=col>seurat_clusters</th><th scope=col>cell_type</th><th scope=col>condition</th><th scope=col>region</th><th scope=col>patient_group</th><th scope=col>global_id</th><th scope=col>rep</th><th scope=col>patient</th><th scope=col>region_novel</th><th scope=col>patient_id</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>CK171#AGGCGTCCACCATTCC-1</th><td>CK171</td><td>62095</td><td>34713</td><td>CK171</td><td>6.245</td><td>14669</td><td>18011</td><td>1136</td><td>0.09025808</td><td>1</td><td>⋯</td><td>15</td><td>CM</td><td>not_defined</td><td>BZ</td><td>group_1</td><td>AKK003 No4 borderzone</td><td>1</td><td>P2</td><td>RZ/BZ</td><td>RZ/BZ_P2</td></tr>\n",
       "\t<tr><th scope=row>CK171#TGATCAGAGGTAAGTT-1</th><td>CK171</td><td>90454</td><td>46242</td><td>CK171</td><td>8.331</td><td>24600</td><td>26536</td><td> 912</td><td>0.13673276</td><td>1</td><td>⋯</td><td>3 </td><td>CM</td><td>not_defined</td><td>BZ</td><td>group_1</td><td>AKK003 No4 borderzone</td><td>1</td><td>P2</td><td>RZ/BZ</td><td>RZ/BZ_P2</td></tr>\n",
       "\t<tr><th scope=row>CK171#TAGCATGCAAGTCTCA-1</th><td>CK171</td><td>73832</td><td>39097</td><td>CK171</td><td>9.020</td><td>21378</td><td>23169</td><td>1163</td><td>0.12278870</td><td>1</td><td>⋯</td><td>15</td><td>CM</td><td>not_defined</td><td>BZ</td><td>group_1</td><td>AKK003 No4 borderzone</td><td>1</td><td>P2</td><td>RZ/BZ</td><td>RZ/BZ_P2</td></tr>\n",
       "\t<tr><th scope=row>CK171#TACATTCCAAACCTAC-1</th><td>CK171</td><td>90941</td><td>46789</td><td>CK171</td><td>8.800</td><td>23434</td><td>25827</td><td> 912</td><td>0.13702053</td><td>1</td><td>⋯</td><td>3 </td><td>CM</td><td>not_defined</td><td>BZ</td><td>group_1</td><td>AKK003 No4 borderzone</td><td>1</td><td>P2</td><td>RZ/BZ</td><td>RZ/BZ_P2</td></tr>\n",
       "\t<tr><th scope=row>CK171#CACCACTGTCGCTAGC-1</th><td>CK171</td><td>76195</td><td>40207</td><td>CK171</td><td>9.127</td><td>21954</td><td>23478</td><td>1032</td><td>0.12531759</td><td>1</td><td>⋯</td><td>15</td><td>CM</td><td>not_defined</td><td>BZ</td><td>group_1</td><td>AKK003 No4 borderzone</td><td>1</td><td>P2</td><td>RZ/BZ</td><td>RZ/BZ_P2</td></tr>\n",
       "\t<tr><th scope=row>CK171#GTCACCTAGGAAGGTA-1</th><td>CK171</td><td>79884</td><td>42256</td><td>CK171</td><td>8.614</td><td>22565</td><td>25028</td><td> 956</td><td>0.13525286</td><td>1</td><td>⋯</td><td>15</td><td>CM</td><td>not_defined</td><td>BZ</td><td>group_1</td><td>AKK003 No4 borderzone</td><td>1</td><td>P2</td><td>RZ/BZ</td><td>RZ/BZ_P2</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 39\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & orig.ident & nCount\\_peaks & nFeature\\_peaks & Sample & TSSEnrichment & ReadsInTSS & ReadsInPromoter & ReadsInBlacklist & PromoterRatio & PassQC & ⋯ & seurat\\_clusters & cell\\_type & condition & region & patient\\_group & global\\_id & rep & patient & region\\_novel & patient\\_id\\\\\n",
       "  & <fct> & <dbl> & <int> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <fct> & <fct> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\tCK171\\#AGGCGTCCACCATTCC-1 & CK171 & 62095 & 34713 & CK171 & 6.245 & 14669 & 18011 & 1136 & 0.09025808 & 1 & ⋯ & 15 & CM & not\\_defined & BZ & group\\_1 & AKK003 No4 borderzone & 1 & P2 & RZ/BZ & RZ/BZ\\_P2\\\\\n",
       "\tCK171\\#TGATCAGAGGTAAGTT-1 & CK171 & 90454 & 46242 & CK171 & 8.331 & 24600 & 26536 &  912 & 0.13673276 & 1 & ⋯ & 3  & CM & not\\_defined & BZ & group\\_1 & AKK003 No4 borderzone & 1 & P2 & RZ/BZ & RZ/BZ\\_P2\\\\\n",
       "\tCK171\\#TAGCATGCAAGTCTCA-1 & CK171 & 73832 & 39097 & CK171 & 9.020 & 21378 & 23169 & 1163 & 0.12278870 & 1 & ⋯ & 15 & CM & not\\_defined & BZ & group\\_1 & AKK003 No4 borderzone & 1 & P2 & RZ/BZ & RZ/BZ\\_P2\\\\\n",
       "\tCK171\\#TACATTCCAAACCTAC-1 & CK171 & 90941 & 46789 & CK171 & 8.800 & 23434 & 25827 &  912 & 0.13702053 & 1 & ⋯ & 3  & CM & not\\_defined & BZ & group\\_1 & AKK003 No4 borderzone & 1 & P2 & RZ/BZ & RZ/BZ\\_P2\\\\\n",
       "\tCK171\\#CACCACTGTCGCTAGC-1 & CK171 & 76195 & 40207 & CK171 & 9.127 & 21954 & 23478 & 1032 & 0.12531759 & 1 & ⋯ & 15 & CM & not\\_defined & BZ & group\\_1 & AKK003 No4 borderzone & 1 & P2 & RZ/BZ & RZ/BZ\\_P2\\\\\n",
       "\tCK171\\#GTCACCTAGGAAGGTA-1 & CK171 & 79884 & 42256 & CK171 & 8.614 & 22565 & 25028 &  956 & 0.13525286 & 1 & ⋯ & 15 & CM & not\\_defined & BZ & group\\_1 & AKK003 No4 borderzone & 1 & P2 & RZ/BZ & RZ/BZ\\_P2\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 39\n",
       "\n",
       "| <!--/--> | orig.ident &lt;fct&gt; | nCount_peaks &lt;dbl&gt; | nFeature_peaks &lt;int&gt; | Sample &lt;chr&gt; | TSSEnrichment &lt;dbl&gt; | ReadsInTSS &lt;dbl&gt; | ReadsInPromoter &lt;dbl&gt; | ReadsInBlacklist &lt;dbl&gt; | PromoterRatio &lt;dbl&gt; | PassQC &lt;dbl&gt; | ⋯ ⋯ | seurat_clusters &lt;fct&gt; | cell_type &lt;fct&gt; | condition &lt;chr&gt; | region &lt;chr&gt; | patient_group &lt;chr&gt; | global_id &lt;chr&gt; | rep &lt;chr&gt; | patient &lt;chr&gt; | region_novel &lt;chr&gt; | patient_id &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| CK171#AGGCGTCCACCATTCC-1 | CK171 | 62095 | 34713 | CK171 | 6.245 | 14669 | 18011 | 1136 | 0.09025808 | 1 | ⋯ | 15 | CM | not_defined | BZ | group_1 | AKK003 No4 borderzone | 1 | P2 | RZ/BZ | RZ/BZ_P2 |\n",
       "| CK171#TGATCAGAGGTAAGTT-1 | CK171 | 90454 | 46242 | CK171 | 8.331 | 24600 | 26536 |  912 | 0.13673276 | 1 | ⋯ | 3  | CM | not_defined | BZ | group_1 | AKK003 No4 borderzone | 1 | P2 | RZ/BZ | RZ/BZ_P2 |\n",
       "| CK171#TAGCATGCAAGTCTCA-1 | CK171 | 73832 | 39097 | CK171 | 9.020 | 21378 | 23169 | 1163 | 0.12278870 | 1 | ⋯ | 15 | CM | not_defined | BZ | group_1 | AKK003 No4 borderzone | 1 | P2 | RZ/BZ | RZ/BZ_P2 |\n",
       "| CK171#TACATTCCAAACCTAC-1 | CK171 | 90941 | 46789 | CK171 | 8.800 | 23434 | 25827 |  912 | 0.13702053 | 1 | ⋯ | 3  | CM | not_defined | BZ | group_1 | AKK003 No4 borderzone | 1 | P2 | RZ/BZ | RZ/BZ_P2 |\n",
       "| CK171#CACCACTGTCGCTAGC-1 | CK171 | 76195 | 40207 | CK171 | 9.127 | 21954 | 23478 | 1032 | 0.12531759 | 1 | ⋯ | 15 | CM | not_defined | BZ | group_1 | AKK003 No4 borderzone | 1 | P2 | RZ/BZ | RZ/BZ_P2 |\n",
       "| CK171#GTCACCTAGGAAGGTA-1 | CK171 | 79884 | 42256 | CK171 | 8.614 | 22565 | 25028 |  956 | 0.13525286 | 1 | ⋯ | 15 | CM | not_defined | BZ | group_1 | AKK003 No4 borderzone | 1 | P2 | RZ/BZ | RZ/BZ_P2 |\n",
       "\n"
      ],
      "text/plain": [
       "                         orig.ident nCount_peaks nFeature_peaks Sample\n",
       "CK171#AGGCGTCCACCATTCC-1 CK171      62095        34713          CK171 \n",
       "CK171#TGATCAGAGGTAAGTT-1 CK171      90454        46242          CK171 \n",
       "CK171#TAGCATGCAAGTCTCA-1 CK171      73832        39097          CK171 \n",
       "CK171#TACATTCCAAACCTAC-1 CK171      90941        46789          CK171 \n",
       "CK171#CACCACTGTCGCTAGC-1 CK171      76195        40207          CK171 \n",
       "CK171#GTCACCTAGGAAGGTA-1 CK171      79884        42256          CK171 \n",
       "                         TSSEnrichment ReadsInTSS ReadsInPromoter\n",
       "CK171#AGGCGTCCACCATTCC-1 6.245         14669      18011          \n",
       "CK171#TGATCAGAGGTAAGTT-1 8.331         24600      26536          \n",
       "CK171#TAGCATGCAAGTCTCA-1 9.020         21378      23169          \n",
       "CK171#TACATTCCAAACCTAC-1 8.800         23434      25827          \n",
       "CK171#CACCACTGTCGCTAGC-1 9.127         21954      23478          \n",
       "CK171#GTCACCTAGGAAGGTA-1 8.614         22565      25028          \n",
       "                         ReadsInBlacklist PromoterRatio PassQC ⋯\n",
       "CK171#AGGCGTCCACCATTCC-1 1136             0.09025808    1      ⋯\n",
       "CK171#TGATCAGAGGTAAGTT-1  912             0.13673276    1      ⋯\n",
       "CK171#TAGCATGCAAGTCTCA-1 1163             0.12278870    1      ⋯\n",
       "CK171#TACATTCCAAACCTAC-1  912             0.13702053    1      ⋯\n",
       "CK171#CACCACTGTCGCTAGC-1 1032             0.12531759    1      ⋯\n",
       "CK171#GTCACCTAGGAAGGTA-1  956             0.13525286    1      ⋯\n",
       "                         seurat_clusters cell_type condition   region\n",
       "CK171#AGGCGTCCACCATTCC-1 15              CM        not_defined BZ    \n",
       "CK171#TGATCAGAGGTAAGTT-1 3               CM        not_defined BZ    \n",
       "CK171#TAGCATGCAAGTCTCA-1 15              CM        not_defined BZ    \n",
       "CK171#TACATTCCAAACCTAC-1 3               CM        not_defined BZ    \n",
       "CK171#CACCACTGTCGCTAGC-1 15              CM        not_defined BZ    \n",
       "CK171#GTCACCTAGGAAGGTA-1 15              CM        not_defined BZ    \n",
       "                         patient_group global_id             rep patient\n",
       "CK171#AGGCGTCCACCATTCC-1 group_1       AKK003 No4 borderzone 1   P2     \n",
       "CK171#TGATCAGAGGTAAGTT-1 group_1       AKK003 No4 borderzone 1   P2     \n",
       "CK171#TAGCATGCAAGTCTCA-1 group_1       AKK003 No4 borderzone 1   P2     \n",
       "CK171#TACATTCCAAACCTAC-1 group_1       AKK003 No4 borderzone 1   P2     \n",
       "CK171#CACCACTGTCGCTAGC-1 group_1       AKK003 No4 borderzone 1   P2     \n",
       "CK171#GTCACCTAGGAAGGTA-1 group_1       AKK003 No4 borderzone 1   P2     \n",
       "                         region_novel patient_id\n",
       "CK171#AGGCGTCCACCATTCC-1 RZ/BZ        RZ/BZ_P2  \n",
       "CK171#TGATCAGAGGTAAGTT-1 RZ/BZ        RZ/BZ_P2  \n",
       "CK171#TAGCATGCAAGTCTCA-1 RZ/BZ        RZ/BZ_P2  \n",
       "CK171#TACATTCCAAACCTAC-1 RZ/BZ        RZ/BZ_P2  \n",
       "CK171#CACCACTGTCGCTAGC-1 RZ/BZ        RZ/BZ_P2  \n",
       "CK171#GTCACCTAGGAAGGTA-1 RZ/BZ        RZ/BZ_P2  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "meta.data <- obj@meta.data\n",
    "head(meta.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6b17fb72-ca5f-4b3d-8a50-9eeafa349d84",
   "metadata": {},
   "outputs": [],
   "source": [
    "meta.data <- meta.data[, c(\"Sample\", \"cell_type\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6fc05500-8e73-4de7-86e0-1d86de8b9409",
   "metadata": {},
   "outputs": [],
   "source": [
    "meta.data$cell_type <- as.character(meta.data$cell_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f0c20839-997d-4ebd-9556-f78d8c976801",
   "metadata": {},
   "outputs": [],
   "source": [
    "counts <- GeneScoreMatrix[, rownames(meta.data)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8d51c18a-a0a1-4f6b-ac99-205baf366ffe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>24919</li><li>46086</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 24919\n",
       "\\item 46086\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 24919\n",
       "2. 46086\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 24919 46086"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "39d08e10-87d1-4be6-8c4c-aa76ed7a132f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Feature names cannot have underscores ('_'), replacing with dashes ('-')”\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>orig.ident</th><th scope=col>nCount_RNA</th><th scope=col>nFeature_RNA</th><th scope=col>Sample</th><th scope=col>cell_type</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>CK171#AGGCGTCCACCATTCC-1</th><td>SeuratProject</td><td>10000.003</td><td>21275</td><td>CK171</td><td>CM</td></tr>\n",
       "\t<tr><th scope=row>CK171#TGATCAGAGGTAAGTT-1</th><td>SeuratProject</td><td>10000.021</td><td>20991</td><td>CK171</td><td>CM</td></tr>\n",
       "\t<tr><th scope=row>CK171#TAGCATGCAAGTCTCA-1</th><td>SeuratProject</td><td> 9999.971</td><td>21083</td><td>CK171</td><td>CM</td></tr>\n",
       "\t<tr><th scope=row>CK171#TACATTCCAAACCTAC-1</th><td>SeuratProject</td><td> 9999.961</td><td>20742</td><td>CK171</td><td>CM</td></tr>\n",
       "\t<tr><th scope=row>CK171#CACCACTGTCGCTAGC-1</th><td>SeuratProject</td><td>10000.007</td><td>21186</td><td>CK171</td><td>CM</td></tr>\n",
       "\t<tr><th scope=row>CK171#GTCACCTAGGAAGGTA-1</th><td>SeuratProject</td><td>10000.111</td><td>21195</td><td>CK171</td><td>CM</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & orig.ident & nCount\\_RNA & nFeature\\_RNA & Sample & cell\\_type\\\\\n",
       "  & <fct> & <dbl> & <int> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\tCK171\\#AGGCGTCCACCATTCC-1 & SeuratProject & 10000.003 & 21275 & CK171 & CM\\\\\n",
       "\tCK171\\#TGATCAGAGGTAAGTT-1 & SeuratProject & 10000.021 & 20991 & CK171 & CM\\\\\n",
       "\tCK171\\#TAGCATGCAAGTCTCA-1 & SeuratProject &  9999.971 & 21083 & CK171 & CM\\\\\n",
       "\tCK171\\#TACATTCCAAACCTAC-1 & SeuratProject &  9999.961 & 20742 & CK171 & CM\\\\\n",
       "\tCK171\\#CACCACTGTCGCTAGC-1 & SeuratProject & 10000.007 & 21186 & CK171 & CM\\\\\n",
       "\tCK171\\#GTCACCTAGGAAGGTA-1 & SeuratProject & 10000.111 & 21195 & CK171 & CM\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 5\n",
       "\n",
       "| <!--/--> | orig.ident &lt;fct&gt; | nCount_RNA &lt;dbl&gt; | nFeature_RNA &lt;int&gt; | Sample &lt;chr&gt; | cell_type &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| CK171#AGGCGTCCACCATTCC-1 | SeuratProject | 10000.003 | 21275 | CK171 | CM |\n",
       "| CK171#TGATCAGAGGTAAGTT-1 | SeuratProject | 10000.021 | 20991 | CK171 | CM |\n",
       "| CK171#TAGCATGCAAGTCTCA-1 | SeuratProject |  9999.971 | 21083 | CK171 | CM |\n",
       "| CK171#TACATTCCAAACCTAC-1 | SeuratProject |  9999.961 | 20742 | CK171 | CM |\n",
       "| CK171#CACCACTGTCGCTAGC-1 | SeuratProject | 10000.007 | 21186 | CK171 | CM |\n",
       "| CK171#GTCACCTAGGAAGGTA-1 | SeuratProject | 10000.111 | 21195 | CK171 | CM |\n",
       "\n"
      ],
      "text/plain": [
       "                         orig.ident    nCount_RNA nFeature_RNA Sample cell_type\n",
       "CK171#AGGCGTCCACCATTCC-1 SeuratProject 10000.003  21275        CK171  CM       \n",
       "CK171#TGATCAGAGGTAAGTT-1 SeuratProject 10000.021  20991        CK171  CM       \n",
       "CK171#TAGCATGCAAGTCTCA-1 SeuratProject  9999.971  21083        CK171  CM       \n",
       "CK171#TACATTCCAAACCTAC-1 SeuratProject  9999.961  20742        CK171  CM       \n",
       "CK171#CACCACTGTCGCTAGC-1 SeuratProject 10000.007  21186        CK171  CM       \n",
       "CK171#GTCACCTAGGAAGGTA-1 SeuratProject 10000.111  21195        CK171  CM       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "obj.atac <- CreateSeuratObject(counts = counts,\n",
    "                               meta.data = meta.data,\n",
    "                               assay = \"RNA\",\n",
    "                              names.delim = \"-\") %>% \n",
    "            NormalizeData()\n",
    "\n",
    "head(obj.atac@meta.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e8801d67-e154-47a1-b937-ab306e5197a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 6 × 2 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>UMAP_1</th><th scope=col>UMAP_2</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>CK171#AGGCGTCCACCATTCC-1</th><td>-3.609075</td><td>-1.9669043</td></tr>\n",
       "\t<tr><th scope=row>CK171#TGATCAGAGGTAAGTT-1</th><td>-6.489815</td><td> 0.3751234</td></tr>\n",
       "\t<tr><th scope=row>CK171#TAGCATGCAAGTCTCA-1</th><td>-4.828056</td><td> 0.7782132</td></tr>\n",
       "\t<tr><th scope=row>CK171#TACATTCCAAACCTAC-1</th><td>-7.681391</td><td>-0.7186383</td></tr>\n",
       "\t<tr><th scope=row>CK171#CACCACTGTCGCTAGC-1</th><td>-4.143128</td><td> 0.3801122</td></tr>\n",
       "\t<tr><th scope=row>CK171#GTCACCTAGGAAGGTA-1</th><td>-1.753949</td><td> 0.4684851</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 2 of type dbl\n",
       "\\begin{tabular}{r|ll}\n",
       "  & UMAP\\_1 & UMAP\\_2\\\\\n",
       "\\hline\n",
       "\tCK171\\#AGGCGTCCACCATTCC-1 & -3.609075 & -1.9669043\\\\\n",
       "\tCK171\\#TGATCAGAGGTAAGTT-1 & -6.489815 &  0.3751234\\\\\n",
       "\tCK171\\#TAGCATGCAAGTCTCA-1 & -4.828056 &  0.7782132\\\\\n",
       "\tCK171\\#TACATTCCAAACCTAC-1 & -7.681391 & -0.7186383\\\\\n",
       "\tCK171\\#CACCACTGTCGCTAGC-1 & -4.143128 &  0.3801122\\\\\n",
       "\tCK171\\#GTCACCTAGGAAGGTA-1 & -1.753949 &  0.4684851\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 2 of type dbl\n",
       "\n",
       "| <!--/--> | UMAP_1 | UMAP_2 |\n",
       "|---|---|---|\n",
       "| CK171#AGGCGTCCACCATTCC-1 | -3.609075 | -1.9669043 |\n",
       "| CK171#TGATCAGAGGTAAGTT-1 | -6.489815 |  0.3751234 |\n",
       "| CK171#TAGCATGCAAGTCTCA-1 | -4.828056 |  0.7782132 |\n",
       "| CK171#TACATTCCAAACCTAC-1 | -7.681391 | -0.7186383 |\n",
       "| CK171#CACCACTGTCGCTAGC-1 | -4.143128 |  0.3801122 |\n",
       "| CK171#GTCACCTAGGAAGGTA-1 | -1.753949 |  0.4684851 |\n",
       "\n"
      ],
      "text/plain": [
       "                         UMAP_1    UMAP_2    \n",
       "CK171#AGGCGTCCACCATTCC-1 -3.609075 -1.9669043\n",
       "CK171#TGATCAGAGGTAAGTT-1 -6.489815  0.3751234\n",
       "CK171#TAGCATGCAAGTCTCA-1 -4.828056  0.7782132\n",
       "CK171#TACATTCCAAACCTAC-1 -7.681391 -0.7186383\n",
       "CK171#CACCACTGTCGCTAGC-1 -4.143128  0.3801122\n",
       "CK171#GTCACCTAGGAAGGTA-1 -1.753949  0.4684851"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "umap_embedding <- Embeddings(obj, reduction = \"umap_harmony_v2\")\n",
    "rownames(umap_embedding) <- colnames(obj.atac)\n",
    "colnames(umap_embedding) <- c(\"UMAP_1\", \"UMAP_2\")\n",
    "head(umap_embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bce5d1a2-9a32-44ff-80fb-36d3398d1a03",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj.atac[[\"umap\"]] <- CreateDimReducObject(embeddings = umap_embedding, key = \"UMAP_\", assay = DefaultAssay(obj.atac))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "240242cf-a5de-4d23-b996-ce05f1701617",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yNEgnsjcXob8xgTeRDPCHaVv/3vrbc2NNhQsP2zt7YDANS3X14T7IDY25fsiRzz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XbxtoUfW1yljRZObFtV3yBv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9/f+oEJAYAZM4ZMb95pzfxZm4afn2IN5Bp2\nsX77Mv53L/RTl6KhyTlrvbNk2l8hpNOvt8586ZrzBPzN0M8a99226a9jc9uOMPpw2rBZh7b4\nbTz5W7eB0twxKYd3ncyE98SJzQhB+xGDAzZM3Rhg6j1gxIj6KPfaqhrBY1djcNcuKQ7t4eNi\naGw0d+qYcvPa3AMMIx4/lVhaASB6hbN3KC2HU41CoZD/9Zt88QL5kgXc1Ysl9ouL5W5cIaZm\nlfoYAlXMR0/7eMd5mGWZuCQ5jQkcrK3UAhCsa5ATTgeAJySTZV9pF9ju2jzVV+aVnJdK2fad\niapAMaU0SvDYlYu5Lw7UNqsOQGsDR1ddq7GWrY81ni5YdQLVgyy57D45KNJQctnqCkLlKVFP\nL2+ePWDWkQxiO/Hz4YYAcM9vVwjQ4vOfci0nADDoOH96O2Rf3Hkg9+JGCAHSdfvN81Vv1QG4\nu2fPC4h7+X7imr+vodvlzxdZqZE7RxgC0Oo5bbwjUg5u3J+Udzz5oN9pGdtu0seuJZyzXGur\nagSPXY2hvHJB9S0XEkSuXmQ7dAFA7Bwks+eB42hCvHzFEtVuxNaOvnld8F4kFg8bzQc+5h7e\nL3T2jHQKAlD+yWMlw4hHTygyO02Il6/6BQq5IGtXy9FRSv/P3n3HN1W1DwB/zr03SdMm3XvR\nCZRRKJSWLbL3UsABCMhQUX+OV5y4x+tAX7cioAKCCKIiCioKiIzKaksLbeku3StNs3PvPb8/\nOkjbdKRJm9Hz/fjx09zee+7T0iRPznjOnNzWFa0HKeQUAA8YAFGAXVg2TNNOLSlFPT1+EpJI\n2aM/Nx/jr2dglZLsL9epC/XdmY8oQMybkYtey/+1mrVwgS8KITESHhx6n5/Q1bItE0THTNq8\nxqzZ2j+tEKAVrQ86D7jriwMfTJcAACjS0goAvEaObLn9dXBiYiCcvnjxMsDN4YjYuLh2A1ek\npxcCRMXEtLuDOhq9fl3su8/8uv2b0hUPBgBA9cG9f2idZq69O6i9Nk2IreeQxM562pQpZo8e\npsfdAlTT3yFNIx9f5OqK5U19BgjRYyay3+8FAMAYEKL6D0SeXlzKZQSoTTkG3HAan3Wt7c35\n5AtN+4w1XEVWxNqoG9LSf4IuLL4+U8AxCKGGf6VQtfKda5eeHDhcj5Azx25PTRK0t7EExrj4\nBhXVv8VBjsOVFaifxQdMHM1gl8CryrK2Tw0/oWt7PXlSSrx78Kr53sPCnbwWpX1m9JxuENNC\nLa+PdvL9oP8yktURvc+py3VGnDzN6y6Imv3YgpjGBhAlcHIL6D9y6pxpgzybqgEoFAoAqP78\nVvS5kcvLy1UATama0Nu7/dp3De2IxeJ2zwDov2bDpBc2Ht++M/vBJ6OgfP+ev/TSZfcuaXdO\nqwmx9SCS2FkN5ePTuNlrM1bPl9xosaABIaAZw6wLl5cwi5dxRw9jtYqKHsAsXsb9/Rc0Tlo1\nBiEkMfI2gFm2ReljmkYMjdvkmoTV5bgVZrvn7xn444TihDBVBCP1wNVVAPBA/vVlJQV5YskA\npVzKsh20UO/h4ebpDTQDPNf4L07TbatkE209Ejz5h8rLbJv5D8azOgRTPWL+GPZ/DY9meA12\noUVKzgLPqYXew34Yej8GTAZeCWtx9gOhO+jqOusBQOAxyLw7Db7zzXeWd5SauLq6AoDXlE0v\nLgxp+93gUQZddKijvkOJRAIAcnmHky38lm9YsOn4gZ27rjz5ku93e09wvmvundv+WIcJsfUg\nkthZj7sntErsAPQfvytYupyKiwcArKhnfzoAdbU3n0kY80lnBU88RyeMBZ5v7NtDVIuJd05O\ndHwil5kBleUAAAgxM+a0vjXHYVmN4VXM5Gl8QT6+nkEWxtoUDLjCuYoCSilUc4indVxDVtfA\nS6fz0tUYns8Dolq+7ia5e9ESt3FiMTP/NvbQAeA4oBlm0RIyDtuxHHXl07k//liZzHZ5uhDC\n6OuB9zQ/FFOC96OWrM3cbX4wia4RAECyOsK6gidB7o8dnYAQUAwEjOnhOJyHDo2AI8WeiRse\nXCwwox3J4MGh8FtRSkotxLe/rNx10YY7fA5s37s3da3vN6e4iMfvvbWDm1oqNvOQxRNWg5zE\nRjqsMWZ/PtjwJbtvF38lGfMt3qexTsufPwsAzSO29IhRQDOAUMMEeWbOQmbebaL/28QsXc7M\nWSj8vyep2LhWN9Hv3MZfunDzMU1RCWORm/HKZ4QVIUBLsuY8c27jxssr+9d2NHL6o1/wA0Pi\nz3h41TECbPBn9V5EjLeASb9RxB47AhwHAMDzuKKsmwFp1Nzl89yFJKyyn+0hTafh9VOT/3eg\n8pIem1CgwJkS+gtbrHZaEzjuP6HTBJTxHZ+7iEbUoyFTzGmBICzCdwR4xrT/bQQYIHwhCCQ9\nHcjwZXcMAO1PW969qrt5UH35pVvC4hZ9lNL15+zIJUsigD/+4X/P1jcd0qW9PFIs8Zz5yc3d\nmYRTNqyOguvfvvr47iQ8ZM29iYZv2wzDAIBS2fx6aKnYzEJ67KyGShjLXUyClnkbYIxVStDp\ngKb57Cwj/WcIYZWqxQH/AOHGR7kzp7BOy8TGUUOHAwAIBPTIBKP3xTXVfEZ6i0Mcj0tL6Fun\n8WmXsZqUPrEI3JurUo55+7/Yf2iGxHVHSGSsXPbL+RM+Oi0A7AwOFwweOvxiyv5zx6PqmwYc\nMM/9fRyXlwvW3GfSXbCsVv/hO1hRDwBI7CzY+Bjy8eXzc7nfD/OFBcDxwNDI3ZOZs5AaaOZg\njJWlKG7ka6rbHjc2k7XpO4AfD51KtRz4QYDejrzttfAFi9KO/VrdYV9H+/4veLKIIi/UhA1A\nELUEcn+CqpTWe5gjBIiG8AXgHdsbcYx4+ov//DzjnacShp9ZvWTCAB9UffWvPbt+zRKMf+fW\nIV3/HEWNfu7T+36a99lbU0Ym37VwbJioKvnQnoOpXOSG/1tqsDoCjVq/Lu7tJ/fvz6PHvddq\nOax7ZKQnQPrWhx7GtziHznp9TYKFYjMLeb2wGiq0n3DjY/r9e1pXi6Vp9thReuKtIBKBRt3y\nfQQBADVwcKumUFAIs+Surt5YZ2TSD3L34K6lgYbMsbMUZMHcrlVDjSsomv4wvg4O3zA0ofmE\nK1K3l6OHKAWif9w9K10kuupaN512SlV5qzb5zKu6994EnqUGDmamzQZhexUBbuJOHcdKRWNI\nGjX312/0tNn6bR+Dvqm0io7DFWX6Lz+j5y1ixpu5r5A11fPGNtxD6L3IJZ+UnMxS3fxl0oDW\nBU0QImayx4AF3sOMtiakmCKdP6ABgLMAMAAF0NXhXSdK8EbEwoavMeB/ZDm1rGq8W6SngIyk\nE1ZACSDqdvAZDqXnQJ4NPAcAIHABz8EQOBFEbp1dbymSCW//82/cW69/9v33//utSiv08A8f\ndsdrbz/1+PxIk3In9+mfnD017JVXv/jph49OKIVewdHj/+/zp55aN6ZlFbDI1RumbL7vGMy4\nd3nr5bC3bvp03aXHvj375af54StGv2bB2MyAMJlTBZCRkRETEwMASUlJCQnGO7p6ju6dV3Fl\npcGUVASAkY8vPWo0++uhxmPOLohC4CSmJ02lR5k3hYHnda88aziURieOowYO0n/9hVnNEj0G\nI0A3n6YIubsjV9eyikpfrQowjII0LfEAACAASURBVBo/I03i1rwdnJDnR8lqznp60zy/rLQw\nVK2cU148Ul7bTtsADZ2+DzwGok52o9Lv3sGnpTR+TkeIioiiYuPYH74zcirNiF5/18Sf0lao\nOJ3f6ScU7Sx6WOIzIllx47q6AgB8BdIvY+6Z7TWk0zbnXbl2pKaGw0oALYDEl/mrQt/5vktB\nIo+fht4/UhoKAHrMzUz+8C9ZBgC40uJfhz04zi3StB+MICwLg14BlABoJ2tH0tNwxqsjh2y+\nseJowZczOlpDaytIj531MXMW6ndtb5z/BNCQ4eHKClxTI1j/EJ+Thdw86BGjQGCZqZjcmb8N\nszrk48vMX6x77XmLNE70BNRymiWWyVBIv4sa/awKFQAoaIHhJr/315Tel54i5lkeIFDTpf0J\ncVmp/od9gjtWdnwaFR7FX0lujoKKiGrY4NgIrqNVujZuT/n59rI6ANhfebl2whYNz3oyzsIu\nj5C+HB56Uiar51wAXCKcROdHvuDKUGfkOc/k/Hi2Lo831oH3XL9Zr0QsaH64t/x8Q1YHAEpe\n+0j2d+dHPm3Kj0UQloZA0H4tEQeivfL2+jcvUwlvPWUXWR2QxRO2gIoZIvzPc/TcRa2Oc+f+\nQR6ezPQ5dOJYS2V1AMBfPm/4EFdW6LZ9YmQuvFklJglL4uhWCQTmryQPqZfxCADg9rJCgMYO\nXwpgSdbVfmqFv0bdxayuAZ+aDO1VwmtCjxlPj5kAFAUURY9MoCdNowcPBSdjn9Yt9+fay6r1\nyufzfurwFCxnNf5C165ndQAQJ3G5nhj/5cDo7wYPSEsY4SlgGERNdIveNWj1EJdAAKARcqJu\n/tJ8BNIn+800bCHHYGd1DvPZqoqu350giO7IPPT2m688etvIcU+ewgkvbH2kg23cbQvpsbMJ\nyNOLmXArTkvh83MNj+PyUuTZ5bqQXSRq/U6M83LankXfOoM/fQKTbWRtgCAuHmddw/I6w4OB\nGg2FAQA2Z6XRGO8ODLshdt5QlBMvr+1OzRqe6zyVpyhm4RJm3mIAAJoGAGAY4ROb9R+9i2tb\nLDVgFi01OQDbsOLajtLO9hD7pvzfp1tmXV3hJxSs8m9dOzDcyTsl4blircyNEWOMH8rel1Jf\nNFIa9mbkIgndYmQ8wTWs+WsKUWPcIkwNgCAI0+QdfevlbQph8MjVH7y35aFh9vNxlcyxA7D2\nHLubFArtG5uB5QEwIABECZ/YbPHEji/M13/8XsdVJpHYmV58B5d6EV9Jsezdie5AQMXF4/w8\nXNOYP/3p7b82NuHTK+dnVpYCwCU3j3nxk6qFwhezrjyVe814YkdRHffJUSGhIJVSYVF04lhw\nMnHIQaPhi/L59FSgaHrsROTtY9rltkHF6aSn/o/v8CURATAUXT/h/V5eqZqqKL4v85skeR4P\neJhL8I+x94d1fSsAgiD6EpLYAdhOYgfAp6fq9+0CrRYoipm9oGHrWMvf5Uah/vMPjS6PbUD1\nC+cL8gAatr6ggWPJfmPdphXUCDgJxXe+7LSLOISCpyysEwh4QOFqJYP5HGcJDwgAQjSqrFNH\nUIcbUXQKuXsIH38GhJ2spXA8OerK6HObu/KXXjLuzQBhr60AhOO1mdNS/sdhDABiSnhp1DMD\nnf177e4EQdgXMhRrW6jBsaJnXubLy5CXN2i17He7cXUVCotgbp1ufDJT9+4SHMrMXcge3Nfe\nCY1ZHQBgDA1ZAtlLtrs0ooo6wXXf2kTzC6DoKSrLRSpl2dqmVQt54hZlL0aHhtJDhvPJF4xd\n3VVYVsunXqbiR5vTiD16Of+X1uW5mjCI5jHPA6YR1V/s15tZHQC8d+PP5megBus/KT75QfSy\n3gyAIAg7QhI72+MkpvqFg0aj++AtLJcDYCjIw2WlgtUbLHgTOmEsfy2Nv5YOAIAQCBjQsx3N\nzSJZXXdJlVFSAI2oiqXlElX3p0alSt1vi59Q5OSMAIdrVPli58bCIwDfxPQv0+v7i8UzLidx\nKRfNj5kvvkHFm9+MnclWVRp9Cgx1CXo1csFjWfvztNUJrmE7BnayfNjiZKyqeWgFAZKxqo7P\nJwiiLyOJnY3i87JxXfNkecxnXgW1GsSWW2yNkGDVBj73Oq6pocIicHUlu2s7bq40SwBYas91\nCjMAINJ6ISGqc8lwUw7s9BKj7hs6qsRJDAAYUJ6TM4MQizECeKZf8J1+PgCAK8p1x45YZLdf\n5B9ofiP2pUqvuK4ub/W7c6aEr0TMvy9wojMtnD8mlseYssZq8Xlesadk2YAAYcRjfn47lZDN\nV5O1u/T8i5ymyjV8Qci492kR2WaQIOwPSexsVdv9JSlTatOo1dz5s1hRT0UPpKLbXaRNRURD\nRGPjKDQM51w3PVAHhaAxtbNYe0ik86pxvubWrQFZjNAVV3fO4MJnaZjs7e4fFuEjFDybV5Cu\nVI1QKTZStMQSNeTo+ETzG7EvL+YdrtIrmh96MS5vRS2+0y9BbFCFxCpZHQA8FjK1ntPuLDsr\nQoKHQ2693WeExW/BqitkuQcLTz0AgAFDTeZOjSxz4OJzFr8RQRA9jSR2NoqKiETePri6CgAA\nY2rYyE43BrhJo9F98DauqQIA7uSfzLzF9PhJHZyOZbW6998CU8qeOT4MqAeqPAbIxnfvQoRx\ntLI+w8WVb8othp37O7GyFN86bbx3yMV6BY3gJwwnRk448u8JAAwIIZETePvg4iKT+/D8Ahqr\nmfQlx2qvGf6aajjVMEmIYVZnRTSiXg6f93L4vB5qvyZzZ8HJdZjTGR5UlSfVXP/GM/ruHrop\nQRA9hBQotlUCoeD+R+gJt1KDhzJzFgqW3MknX9Bv/0S/41P+6pWOL+XSUxuyusaHJ/7o+Hw+\n5VKLrI4R0LdOR35k2Z1t+TjtgivXOFZ+d0nBrMpSwDj54vmL9QoA4DAAwHEv3wIXCQCAyIm5\n6x7BshWoC5vAtsKM6nPddQCgx5zhQwzwddlZawXTm3R1eQXHV7fK6hqUnnuWzK4ljOAwXJbB\n7kL4IBs+zYUfSqCw1+Z9KrMObXlw8YQh/bylTgJa4OTqGz7s1mX/+eRkieFYxdG1EoQQQrEv\npxtvRv/nOj+EEELj/1fW5nulZ756bvWchP6BXhKRSOIdHB078fbHPjyUJrOTZwPpsbNdSCJl\n5jTu/80nX9Tv3QkUBRjzWRmC1RuoAYPavVLd4jmGNRrAuKPys6y+1UPu+O9mBE70iDG1VdeT\nz6XOX+y1b9dARWMRXbbNZzO08TGhWol8/bBCod/5Bda2W9TGOIqiR/a59bAAwOGWRf5wmyOO\nqL74r5zDs3A7P6lWUZC8TRo46hXfYY/2cmCE7bpaD18XQKUWEABCgAEwhp9LIc4d7gkF1x7t\n5K49+uC4RR9f04gCEmcvXBUZ4CFQVeWn/PHLgS0n9u889PHpX+6PNhhsoCjqyratZ597f0yb\nLizloW37KiiKalPbU5fz7cZF9267ogIn/9ixtywIdafqy3KT/zn43qnvP3xn3lsHdz860rUn\nf0RLIImdfeAuXzAoMIu4439Q0QP51Et8RQUVHELFDAGEcL2cT08FhFBoGNA08HxDPkcNGtrx\npgLU4Fg4dhR43MGnc+Tp1Vwdl7AWN2/PyWHhWk7fWJUDUcN06kFip2tqDSAEgBNdpVFeXgi8\nAIA9uB2XlZrUPpJIBeseBGfnngnfplUaTLADAARwl581S1p2z5m6nPP1Bf3FvjO9Bndl6U/R\nPw/zfEdLpni98saZx5x9RkgCb7FcmITdOlcDX+Q3ft2Q0jVLlkG+Cp4ZAF4Wq9nZWsq7D318\nTeM796uzP9wTcTN7wRV/PTxl5ke/Pf7Iztt+WX1zf5fhCQlXzu3aeuTNMXNaFQur+XbbD/WR\nI+KrL7QsDVV7ZOP0u7flMlHLPtnz4fpRPs1ZojL30EvLV7/982Oz1gVn7Fvi2UM/oIWQoVg7\nQVFw81M15vNytC8/o9+7k/vzqP7rL9ifDuDyUt3br7A/fMce3Md++Rmz6A4UGIxcpXT8aEFn\n+zshvwDk4dFuVocQODmBW68W7iKMoBASO4NQKFh+L3J1AwAklbrcdc8fw4euD/Qf5yp9OCjw\n5yExzW/mfFF+R7PrEEJ+/uAkBtSYANAjE4SbX0P+AT3/k9iiIKGb4dqI/4ROHecWacV4TKXm\n9YkX3xx36e1Hrn83O/Wju6/u6MpVurrsrgy21pecNDtAwv4VqWF7PgAYf2HBADI9fJQDfDcG\nLCs+mSxAqN8TSa2ulX05xwmh4EdP8wCgv3o1G8BjwUOGWR0AIN/Jb37yzkNPPb8gosWMAvHM\n+dNEtd9t3d9iN0YAKNz5xe/a8EWzBrbcMpP9940Ht+XyHrO2/rX3foOsDgBcIua/9du364dE\nDxYUZdQDAABffnrrE4tGDwz1kYrEbv7hw2es/+/RfJuoLEF67OwDnTCm9dQ6g/FW7uwpUChA\n1/g3jdVqXFwofPiJLjaOq6twdTu9cQIGWBY0GpyXa/wEoncgBICoMRMBgIrqL3zmZaxUIBcJ\nAAQCfNbfSAqCPL1xaXGrl2AkkVIxQ+ixE1FgEADgqgrur9+xrJaKiKZvmdIrP4mNeiNy8bL0\nrQ1fD5eEvBQ+37rxmOrJnIP/yvOaH+4tP/9E6PQ4SUjHVzEugTqDq9oj7Kwdok/4vhh43NEH\nAYyhQAXnamCsqfvd+d6xcsajx3/5bt/ZtxLH3vyAVf39nj+0EL1i1TgKAATh4cEAVXm5ZRj8\nW/ZHO094+IMJrRvViWffPfvVXw9t3V2yYqNBBae0HduT+OjNy4dnbGkR/ckvv8oFGPLwW/eE\nGOvtlk77/EpWc9unN028ZUtOwITVqzbFBjjry9N+2/X103MOpxxI2bvI2lsqksTOPlAxQ8BJ\n3MHCVcPVEoAQrm/9EaUj7cyvRz5+QFG4ooxMoLYm1DjkgTw8cWU5BAU3LFlFLhLQ69gTx3DO\ndeTuSU+Zjnz8DK9j5t+m//Iz0GoBgAqPpBPGgqsbFRltOC6PvH2Zpct7+QeyTbf5xF1JeOGP\nmqu+QtfF3sOFvbsVrPkOV7VeU1Wqres0sRO6BHWa2CFa5BFFNrro8xQspMk7fytAAGe6kdiB\n5+0r52z85Yfvvj27ZWxzZlf23Z7jeohbtaqxcuPoZz5f++P8bQ8nTLv2xAPLZt86KtKjoyl9\nPO+y+N6lHj98tXX7tY2bY5qOnv1iRxqK/+/qYfwTLX6c7DNnKgFCZ88Z0oV4k/bsyuICNu49\n8dH4xoHPxx6dsX78kxf/OiNbtMDKBSDJUKzdoHw6+hTAG1a14Hkqqt3adW0hVzdqSJuSpwiA\nokBZb07BW+TjS4WGdTzDj+hE068f11Sz3+3Wvf8mNK2HYL/fxx07yufncskX9J/+DytbzBKj\nwiOFT74gWHGv4L7/E2x4mBoxiorqT/4tOhDj7P9w8OQ7fOPtLqsDAH9RixndQsSMcu3X6VUd\nT7BrgDlt3u9Luh8Z4RjyVV0aY8UAucrutC+Zv3KROxTv3/dP86Sj0n17TnDUmFUrm9/OPGdv\nPf/vzifGqn56+vZxUV5ufoPGL7x305Zdf1yr4Yy26jRr/YpQSN2+NampVe1v23YXMVPW3hPe\n+tzy8nIACA0N7Uq4QqEQoOriibT65kPuC7emZV780NpZHZDEzo7QiV0rgSYU0hMn06NNq5cm\nuPMeYFq+mWHA5aVYZdYidqzTo4gokNr8IiL7gcvLuH/PAABwHJd6CQAAY8AYK5U4K6PVychF\nQg0ZRoVHknzO4T0dOhM1/SvTiPpq0D0+AmmnV6kru7QBXV3Br1k/TtAri80KkbBrii5XPtdw\nwHajO8BpzsolXlC2f9/fjTlY4bd7TvOCKavuCjY4C3mMWPHat2fya6qyzhz6dNPCIYKcQ6+t\nmT4oKHzqEwfz2tYAoMasXzMUCnZu/a3he/UHt31X4zx37Z1t63kJhUIA4DjjKWIrCQ88P8uH\nPbc5PiJ2xson3vry14s3VDYzskUSO7tBxcZ15TRm7mJmzkKT38gZhgoJM3K8zWJw09TVcieO\ngdyUcWGiM1hW2/Rly39lk/YmIRzLPO/Yi/HPPB82578Ri0rHvXmn76hOL9ErbmC+q+/WitLT\nRac2mhcjYc+kXe7GdqKB6c4nScHUlXcEQumBb//mAABy9u75F4vnrV7mbexkoWf0mLn3PP76\nZ/tPZFSUJ+9a7vbPO7dP23Sy7XSlwfeuG0vX7Nt6QA4AFXu/+FnhtXTtIiO9DUFBQQCQk53d\nlWBR9LqfU09vf3rpEN2Fb995cs2c+FDfiCkPfZmi6PzaHkfeCeyHSATizutQIO/uzNvElRXt\n1bIibA0VHgkAQNP0qNEAAIgChJCbG9U/puMLCccWJwl5KXzek/1mdKWvDgBwF8ZhDU+X3/iz\ne4ERjiDMuUv9BQhBtEs3b0GNX3FXBJR/v+8EC3B9z54L4LZo1aKWBRmwkblBjOew5Z/+d4kU\n5+z6xkhV8eAV62eJlYe37S2D3K+3n9AHr1w309i88qAJEyIAKn76/lTXPu7Q/mPWvL77eEZl\nbeHFo1++unqI8uRHa6au+76q80t7GEns7Ak94VZjR2lAjf+O9MgEKjLa1Ga5C0m6La/hfLPW\nvSJXU8Zbychge1DHT0lET5nZPCGSmX8bM/82atAQetwtggceA7G4FwIkHIa6ppM9bFrjeUXZ\n6Z6JhbB5LgzEuXX+0o1xN1ZONEu8Z/kAqDp44CS+9u23qeC3bNWsmxXoLr81daCf+62f3DB2\nJafVsgB1Ncbm2rkvXb/Enf37wE+n93zzL45ZvXas8S0TE1beE4OgcOtjb6Ua2YgFoPzgPcOH\nL37+txIAAGCVqoazKJeQETNWPbv9n6OboqHq4HfHLbBdt3lIYmdPmMnT6YmTgREATdODhwqf\n2Cx67V3R6+8Jn3lJsHKt8OEnurPCEWP20AFzVkg0oMbeAnSX/5zMvp3D6rDflAoIYKbPvvmY\npulxtwhWrmXmLUbuHj0eG+FYyi68YtL5PKfK+mF85ZUPeygewtbdFgQM6ii3QwCREkgwo3zv\nkJUrhkPFr798vX//VQhZvnqKwQBwzMhoVYX85PN3vvB3Zcv0TVv08+Mv/qgGwYRJRnM28ez1\ny4P5k58/9G0KGrN2Tbu7Ng3ZtOPJoYzmwnNTZ7zwS67hqK6u7PT7yybcvTMlv84v2h/4c88O\nkLjGP3O25S5PGABohrF6XmV/i7/6NISYOQuZ2QsAY8MJVcjVDQ2O7WabOh2YuuuUMTg/Bzgy\nmNujEL2ALE4kLEZTl9X5SS0hQKXnX/AZ+lBPxEPYugAnWBcGn+cBRkZWyCIEXkLYGNGFHU86\nELli5djNj33z5McVMODZVaMNcySnKW/vfyV97vOnXr4l+PO4yZOGh/m7CzXVpTkX/jx+tYaj\ng+Z+9tl9gUZbpcetXzP4o5cvpwunbV8Z1v7dnUa/8usP7LJ7tpx4eW7/9yMTxo2I9BaqqwrS\nz57LqGWFEYu27N2+MYICSFx+36iPH9syPe7qXUsmDvRz5uuKUg7v3nNdOHDTAzNJYkeYDnX4\nmclUIhEIRaAzltvRNHRtgRAA8BlXWx2hvH34qsquXY1IqbzOUQho8oQlLIBVV5Rf+m/H3cNG\nYcCsrg7zOkT12LZRhC2L9wA3AXxdCCXqFnvFIoAED7g7BCTmvkaF3rXylk33naiAkY+ual1Q\nTpL43PGr0/d88vm+I6eTf9/3p1wDjIt7QPiQhQ/OvfP+9YsHubX7zjh07brRrz6SvmCt8bUY\nzZjguW+fvLrs4NdffvPDiSuXfr9co3f2DQqJW/rU0pVr7p4RLWk4DcU8euR04Dtvbz34+/Y3\nd9VohF6BoQOnbd772EN3DLf+lBhkbCZin5ORkRETEwMASUlJCQn2t0GkmXRffISzTf7sTvQy\nhBB4eNKTptLxoxtqFBNEN+jqC6/uG8zru7l6D9HCuPUW6OMn7BiP4Vo9pMuhVg80gkAniHOH\nAKfOLyR6BekAIICO6s+amdi17XFr2KWesByMMdRUswf38ZnXBCvXWjscwl7l/7ncaFanoKHE\nCSiAcCV09LmB19flH3ILs7Mt1whLohAMdoXBpECpjbL6WDBhA7z8Oj+nY62zOgCGQc4uZPVr\nT+DTU3FtjbWjIOyVosxIRYhqIfzpDelSuCKFo76gb/+JizHO/X2pXlXagyESBGEGktgRACZt\nLGsMFdxy8yKBkJk2B5ycSKddT9FqrB0BYZdUVZcBG6nGUCAG3JTM6dHNr43CnLb0/As9EB1B\nEBZAEjsC+ItJ3b+YolBAIAoJBWiqTocQFRTC/vojrqluOon021lE468X+foh37b74RBEJzDm\nyi+9YfRboerGbncnHnx0IOxsWUVtzncWDo4gCAshc+z6PJ7nS7u1BWTDvDqex6UlXGkJ8vAE\njQZrNdTgWKiXt5x215V+uzbT9BoPkAWzjVC/MFAqUFAIM2s+2T2M6Iby5Ldrc/Yb/Za7HhgM\niTLw7dq6CF6nwrweUQJLxkcQhCWQxK7PoyjkJMYqpckXIhrwzWIouLaGGjZCuHQ5MIz+8w8A\nYRPzsdZnIw8vQAjXVBtpRyAEvdHK4A4L0ZRw1QZw7nxPOYJojyz7QHsflGgMibXg29VnFXIN\nmU6yOoKwTSSxIwA8vcDExA45ibGm9TQvPuWSLjuTHjsRDRoKudlm9bUhCtdUtzuG28eyOgCg\n+g8iWR1hLppq7ymJoGtZHQKKErqFLwoZ/4FFIyMIwmJIYkcAkkiwidVJsEZt/LhSyf5xBAmF\nAOaNoDaWTm1qom+PxyKhEA0cBDxPRmAJc7h4DleVnzerCcT0X3TW2WcEAHBaGcYsQgxbUibk\nAlGoBITk75MgrI8kdgTQw+Pb7hthDqxr+uyPEAiFFtiyrA9ndQCAdTr2h+/4S+cFGx4mpYmJ\nbqsr+t3cJni2IuW9fpO3F/y1pub6NwCAgMLAO2lCI8o+cHpwOoRYv+w+QfRx5AMWAVRcPLN0\nOQoItkxzhrXrMCYVTyyFL8jj01OtHQVhr1httU5RYH47Ndd3F55Y15DVAQAGHgA0TjcKvV+E\nXYXmt08QhJlIjx0BAECPTKBHJvBpKfqfvge5zOTrhULgOOC45oonANCY0ulaztzp24OqZsJy\ncysOEn2WLOegpZpSlJ0FigbecCNpXuVyFdJUAKAo+bu+5LhQEuoZfReiRZa6KUEQXUQSO+Im\nKiIa6XWmzrcDhhZt2oz1LHf+LLAsNTQO6mrZv37HNVXQZoEFYJLbdR8VHmntEAh7pZZds1RT\n2rrstrvNCDWhEOJckfLujTOPNxyqSvuk/+LTiBJa6r4EQXQFSeyIm/jCPKxWdXISQiB2BrWq\nOfmjx08CqRsCYGbMbTqpn3DocPbQAe7MqRY5IkLg5ARq4wsviE4IBCjQQsPlRB/DsyqZJUsK\nt/1khsVsuH4JXXrypeaPbsrKC/KCI27hCyx3X4IgOkcSO8KAU8uJzxRFT5zMnz6J9XoAoKIG\nCFauBaEQq5T6Lz7GpcUAQIVHMVNmGW2MGjCIO/234RHk44sx7oHEztH7ABECjOkxE8jeuyYp\n1NQUaGqGSgLdmb5eKaau4Be9olt1yLtM5nq89o/oVgf1msoevSlBEG2RxI64iQoNo8Kj+Lzs\nhof0mAnMrPkwaSpfWICk0ubuIuQiEf7fJlxaAjSNfP3ayzaoAYOoYSP4lEvNR3BFucVjRj6+\nUFuDWSPbXzoOJydm1nx61Bhrx2FPXsj7+ZWCXzHGElq0d/DauV5DrR2RNXFa0yfOmghjru1B\nWd4PCFGe/VciirzXEEQvIU82wgBFCdY+wF1MwlWVVL9wanAsAIDYmRoQ0/pMhFBgUOftRUUb\nJnY9AVdW9Gj7tgDp9XTiOGtHYU+uKItfyf+1YfMTFadbdfXryglvoz65ZzGnlemVxdKAiRTt\nhHkdxp3tAtt9RnrN5QVH5AW/1pecDJv8dY/dlyCIFki5E6IlhqETxzFzFlJDhpk/8EdF9LdI\nUDch1O52FA6MjMCa6KqytHlLOx5wNaso19VbNySrqEj9X+pXflf3Dcn6aYL3kI1O7gMs/PRB\nFC1yD4h/DtGidv5KMQDUZO5itTWWvC9BEO0jPXZED0LePlRIKF9kuepWXVmu27oQg93DQSHW\nDsHODHUJQk3bFVOAvAUSP6HU2kH1No0s48aZxxqeMnp1ZUXKFsvfA/OcVlZ5dQeiRZhrrkPe\nds4rxnoVRwlU5f/STp7O3nGWj4QgiCakx47oWcyqDcjN3XLtdaG/wcFKIiMQTJ5h7SDszCCX\ngNcjFtAIAYAbI941aHUfHIdVV6X0znOBVZXwesMO0dY3ZQTSusKj6bsjrv88NWP/iJxf52He\noSfFEoRVkR47omchiVSw7kHdllcttG61C6304CyiXocQHZ9gZI4j0Zmn+s1cEzCuQFM9yCXA\npU+WyUW9OYLfYQbJ6usLT64D1NiPUFdwuOziKwGjXuqVyAiizyE9dkSPQz6+gqUrml/WCRNg\nzJ1P4lMvWzsOu+QrlI5yDeubWR2nkxeevN/aUbRk8Imr7NIbOnlex6fLcg7kH7u76O8HNLUW\nK61MEH0B6bEjeoWbm0N1pPUmBPz1TCqWTEsiTKCq+NeW1ytgXl+Tvdd/xDPtnVCZ9nHRqQcB\nUYBxTebO6EX/1N84pqvPlwTe4hF5e19cQUUQXUYSO6JXtNox1nwIAQYHr0vcACOQSKwdBGFf\ncNW17daOoRPll99x8U1w8ogRuLQunMSzqqr0zxCghuIsHKvMPjyDVVcgQJVpH6viNgWNftMa\nIROEfSCjY0RvoMIikIvlshOaoUeN7jMf2jG4uFg7BsKe1Gbvq83+1tpRdILT1V7/eVrartDS\nCzcn22lqr2XsH5n8hYum9ho2+NjGqisAoOFIZer7itLTvVBymSDsFEnsiF4hdhasf5CKiAYL\nFKBH4OIMzs5AUYCQ49d4abCWFwAAIABJREFUQwhk5D2MMIGy/Fy3rrPCUwljvvT8S+rq1IaH\nub8vU1WnNBwHAEAIEAWINryE57RZP46/8nVATebOXo+XIOwAGYolegnyDxRseAgw5k4e407/\njTVqhBHWazu/sjUMcjl34s/GR+6eiOewvM6iwdoSjJs3cyOITmAeECWUhnfjUgrRPLZKFRKc\n+eMEaeBEv/jNmporzQcBQCQNo0UeQrcoef5hzGkNdy3DnLbwxHq3sPm0yILVlAjCEZDEjuhd\nCNGTptGTpgEArq7Svf2KuaW2ZDXY8frsEABFA8cBABUXT8fFWzsgwtbJC48WnXpQV5/n7DMq\nZOLHItcIrTzXpBZ46y1v4nXyuoJf5YVHWh3XyvMB8lSVlwBAIAkBntWrShu+hQFjXquRZbr4\nJfZytARh40hiR1gN8vKmBsfyaSnmNtRBZoiQfdYrpgTrH0JCEYjFyMPT2sEQtgzX5nxXm32g\nLu8njFkArKxIun5oavD49wr+Wm1iU1Zdt455Y8/Um4f0iiKvgWtqMndi4ABjAIQogZO7pTct\nJAj7RxI7wpoEy9ewWz/gcnN66gZ2mNUhhmHufYAKi7B2IISt4zlN5sEx6qrkVsc5nazk7FPG\ntvayb9UZOwAAIYQBKEoQcsuntMjD2kERhM0hiycIq0KIXnB779zILpZZ0FEDBI88RUVEWTsQ\nwtbVFRzO/nl626yugV5d7mBZXTMMQAkk0tDpQkm/hiOsprou/5Ci9B9H/ZEJwiSkx46wMv5G\nUW/cBmPbT+yo6AHMvQ/YfpyE1ZUnv118dlMf/UPBmNcr6vIP1+X/EjZlZ3XGjvqSkw31z6VB\nk6PmHkWUwNohEoQ1kR47wsqQu9lzyMTizs+haETb9McY5OcvWLWeZHVEV5QnvwOIdE9BwZ8r\n64uPN+9qU1/8V831PdYNiSCsjiR2hJVRkdHIy9usJtTqTk+hp82k4kaadZcehSjBshXAkJ4G\n02CAnWUVC9Ku3XU1K0leb+1weom2LptTVzpIWkfRnZ/TLozb/Ba0ddnmhEMQDsCm+zCIPgGh\nhroenWKmzeYL8/nMq6bfgmLGTgREASDu/FmTL+9pNCNYtQ4FhVg7DvvzcXHpQ9dzaQQY4Puq\n6qQRscMlDr5LR13BL7lHFrVNaOwSRQPfped+12Gerc7YIfYa7uwzwrItE4S9IIkdYQNETp2e\nQo+7hZ4yg+Z5/Sfv8TcKTWqeHj0OnMQAwNx+J/Acd/HfbsZpHsTQmG3xNobELvTchdSAGCR1\ntUpI9m5HWQVCwGEAAD3m91ZUOnxiV/T3/RjrrR2FhVg6qwOA8sv/bfjCL+6poNFvWLx9grB9\nZCiWsD56/KRWR5Bby2ryCDEz5gJCQNOCjY8x02aBi7TVdDSEEHL3pMdOoPoPaHGt1JWZu6j5\nETVsBNAtRn+okH7I1a039lNy92p1gFmxmo5PJFmdWQy6ruywuI1paq/v1Sl6ZbGR/Su//N/8\nP+7CvM7agRBEbyOJHWF9dMIY5va7kVPjGggqLl746NPI3R0Qasi3qCHDQCRqPJui6KmzRM+/\nJnr+dXriFOTS1EPj7CK4Zy2zYIngztXIvSkvREiw4HZgGnum2SM/63d81mrkl0ocK3z2FXrS\nlKYDLTM8y61mwFUVyEXS3CAVEkpFkvKqZlnt74sBKEAUAoZCd/n5WDuiHqQo/Sfv2N3WjsKe\n1GTvLb/8lrWjIIje5kBDsUfudZm9Q9X2uMs9RxRfzez9eAhTUNH9YdZ80GqoQUORjy8ACO57\nhPvrN1xdjcIjmFumGrnG2YWZswDmLMA3CrFKRfULb0z+nJ2Fjz7NXToPajUVMwQFBjWcjisr\nuJPHWrWBgkIaNuxips0GluWvXAaFArea80fRABj4Tuvyt6gHizy96BGj2D+PGnYpUcNHYIUC\nl5dSoWH09Dld+c0QrVTq9YUa7QBnsYSmHwwKkNL0/soqKU0/HBTo2OOwlVc+InXaTETVl5zw\nH/mctcMgiF7lOImdXiZTAYRM3rB4aMsJW6KEMOtERHQVn3tdv/1TYFkAQBfOCTc+jvU6JJEy\nt93ZlctRcGiLXjWNhi8vowYPQ25uhofZg3tbjdUxU2bQU2cBRQEAMAwzbzEeNUb3Xst5ORgD\n7sJMIITo+ERwkXBn/gaOp0LCBGs2gEjEZ17jbxQ1l2OgwqOoocO78kMRRm0pKn4qt4DF2I2m\n9wwaMNvLY5W/7yp/X2vH1eNYVZlG0WMbtDgqxAtcAq0dBEH0NsdJ7GQyGQCMWP2//y3vfCY+\nYVO4335p7g/DFeW6La9iuRwoqjHxMgWfcVX/zQ7Q6YBCzJSZhpfzRS2XXDAMuHmwv/9CBQRR\nsXENI6R8evsb1yKqOT9rvQWt2JmOT2SmzwahiJk1v8VNFt+h//JTLJcDAD1iFDVkmEk/DmHo\nulr9RG5+wy++nuOWX8usHJdIO3rlP8zrr/94i6Lc9lZz2z4MsryfUnd4ecXcGzj6DYTMKa1C\nEHbDcRK72tpaACcPD5LV2R9cW2M40NmQBgGP2T+OoH4RVPSAdq9sRaHQf7W1Mf3iMXvsKDV4\nGAoIBADu0vmGHsFmyMubPfgtAHAAVHqq4K5VAMCd+ae9tpGvLz3hVmD1yNOHPfQ9rqoAVzd6\nyDBq2IgO9nVFgUHCTc/zJcXIRYK8HXkGWC9IU6qa02keoJblbmh1/ZxEHV5k9/KP3UWyum7j\ndXIeoDz5bcbJ0y/uKWuHQxC9wXESO5lMBuDu7t75mYRtwRhJXXGdrO03AAAX5kOXEzvd1g9u\ndqoBAMa4rAQFBOKaanb/N4bzk6igEL745upCPuUSnjkPeXphVbtFbqmoAfSoMQ1fC594DvR6\nEHStnrBASPUL7+KPQHRAalDMlgKQ0EywSGjFeHoaq63J+21pffGf1g7EEZQkbRa59XePWGzt\nQAiixznOqtja2loAF8XlDzfMiIv0k4rE7kFDJq9+/Zd8strdtrFHD3dQl86ETSnkclxe1vqg\nSAQA+EYh8LzhvHN65tzWZ6qUAABi47PvkVhMT5zc4lAXszrCQq6r1QvTMpofChH6KibKgcdh\nWXXF1W8H2XtWhyhb6U/FmM3/cwWn7yvbkxB9mcMkdlgmkwPkbHt480l99OS777/vzlv9Kv/+\n6tm5I2a8m26snOeqVavim9x+++29HjABAAA8z/1zor1vUtEDqNi4rrZUVND2IPv9XqxSIg+D\n7WgRAqGQCotE7u6AKAAARCF3d+QfCACGFe8MIMHjzyJ3jy5GQvSEHaUVqqZVLBTAcFdppU6/\nOa/wr9o66wbWQ6oydrCqcmtHYQJK4Nz2oMgtAtlM8s2zKm1tRufnEYSdc5ihWH34jEcf7yeK\nWvjIhvE+DS8kWHb2qcmT3jrx9ENfLP3rgeBWF2RkZFy8eLH3AyVa4Nj2qs/Tk2cw02ebUkbO\nSCUIrFDgrAxq+Eg6fjR34Vxjy9Nng0AgWH0/+9MBXHYDBQQz8xtr3dEjRiGG1n/zVYtWRAJS\nQ9jq6jmuuaAMD3CuTn6uTg4ArxYUvRkRtik0yNoBWhinrrJ2CCZAADxrZMtmVlmKbalsdHnK\nlrCp35BVFIRjQzb1rOsS2bapHusMxiemfFF7bG07U+vYX9d4zvlSOedL+eFVrcbYPv7446Ki\nxllW1dXV27ZtA4CkpKSEhIQeCZtoh377p3zWtbbHmTtW0HGjut4OVin1b70CGnWrP2nBHSup\nuHgA4HOuc6nJOPUyVimQi4RZchcVM8RoU7pnH8MGKy3QkGHCFfd2PRLCUjDA7vKKX6trvQWC\nUVLJqozrgFpvL4EQSCi6bsJoW+kXspD6G8eu/zzdrgrXobbRUpQQA8a8De2BRjFit7B5weP+\nJ3AOsHYsBNEj7LDHThg19e67/W8+HhLV/vxpJjQ0ECCztlYO0Cqx27hxY/PXGRkZDYkd0fuY\nO1ZyB/dxacktjiJERUSb1A5ydhGsf5A9+jPOzwOtpvk4u/8bmmVBpWR/O9y85wRWKvV7vhI9\n9gxIpcC0ni0nWL1Bv+2ThgQRSSTCO1Z24+cizPdGwY1n8wpoBDwGN4b+MDriiZx8NW5RKRpj\nUPK8hufFlMNMLAEAkAZP9Rl8f2X6J9YOpOuM5KA8r0PItv5deFZdm/1dXcEv7mGL/OI2ib2G\nWjsigrAwO0zsnCc9tXtS64Oa5F2v7zrDjXn2tdsNx1xV6en5AOLQ0C7PwCd6G3JxoafNbJXY\nMbdMab1dbFeaCgwWrLkfOI79/VfuxB8NBzHHsd/vbfjS4FwMOp32vy8CRdFjxjPzbjMc80VR\nA4Qv/JfLTEfOUiq6vwV3FSNM8llJKQBwGABAxnI6jCe5u/1eK+MMeu0oBFM83Bwsq2ugqk7u\n/CSbh3GnW7ZYAa9X1mR/I8v/cdCyNKG0H6up0iuLRW79KUZs7dAIwlx2mNgZ5SQtOvzeZ5f3\nyePG7L49qOF9mK889p8XD2rB/c4lU8kKRluGfP2Rrz+uLAeMARDy8DRruy2abj0lroP5BjzP\nnf4bBYXSI1sOwYvF9PD47sdAWEIN22L+pYrlXwwLPVEnV3McNI38OSFqXYCflQLsQay2RiO7\nbu0oHBrGvF6ReXCce8SiqqufYZ6lnbwipu+XBt1q7cgIwiwO8zE38qFPno93Kd6zdPDQOWse\nffLJh1fNGz5wxqcZdMSKL7Ysduu8AcKKKEqw5j4qNg75+FKxwwXrHwTarNnNKMCk2TMI5+ea\nczuiJxyX1Slbbtr7XH7BA9k5R4YO+iA6woVuLHWiwXhdRnY914Vt3+xK7pEFnKbS2lE4hA57\n3PWqksq0jzDPAgCvrc378+7eCosgeoqj9NgBSEe/+Of54R+//dG+v4/u+LNKJ/IOH7bkqbWb\nnlw5ghQttn3Iw7Nh7weLoCL7I78AXF7a1DoC6KDfDoNUYqlbExZxuk6+LjO77fFLcsWj2bnv\nRoU353w8xnUcd0WhGusm7d0Ye5CuPl9R2u4mKIQJUJvlNq3d/C7GPKssZdWVjJhsEkPYMcdJ\n7ADANWbh0zsWPm3tMAhbILz/Ed0XH+KSYsC4s1d2oGNH9k5URFcUa3UzU9OVvJG5WRggWaGc\nl9piGTUCCHFyrC0obGzBgR0ztewDovTKGySxI+waefkgHBP7y4+4+EbnL+sIoaAQ5E8KH9gE\nHsMXpeXz064pOL69fzqEoLlScYP/hASFiGxlhwOLEEpCXYOnWTuKbrPvxUaFfz9g7RAIwiwO\n1WNHEM24lC5Vn6ZHJhrZXoywkjcKbzyXV4A6zAz4lgnfptCgNyPCejQqqwif+X3m94maWiMl\nHm0S5R42jxa5itz6V6S+z2rsqbpyC5hXV6dYOwiCMAvpsSMckVoF+s5roiJnF2bJXWRXCXPw\nOVlc0hlcUmyR1raVlgEAbqcqL0OhI7GDo8VOtEHid5u3Y9YyogVSgbMdrfblZQW/BCa+4T/y\nuZBx/7N2MGbBnJ7XK6wdBUF0H+mxIxxSlwaD6AmkroFZ2H27uEvnAQAQYuYsNPP3WaDRlmg7\nSsd5jENFwp0x/RemXSvX6WmEnusXnODqsAtfXPvNrS8+Ye0ougyzabvDxO6DWZ3M2qG0YmRL\njA5gzF75yj94wgdeA9f0XEwE0XNIYkc4IrEYubljWW0Hp6CwCHrS1N4KyAHh4qLGrA4AMGaP\nHKLHTGjYcrd71mVm66GjYrY8hiHnkxNcXT6OjogWiwNFQm+BI1eo9I19pOTfzdjYHqy2CfOs\nqsYGxzFN3paNY5UFJ9aKvWKdfUgxS8L+kKFYwhFhjJX1HZ9ChUWCI25X0GtwXcuOGY7Dik5+\n5x34o1Z2rFbW6VoXDDhJrliSnlms0zl2VgcAytLTdpTVORqM5cV/WTsIgugO8sZGOCKOBa6T\njYyowKDeicVRoeBQYAQNNQIRQsjTsxu7wDVQ8/yStIyu96sgQN+UO3jx3qqrX2QdmmTtKPq0\n0qTnFeXnrB0FQZiMJHaEI2IEVER0B9+n4xOp2LheC8chIVc3wd2rkEQKAODnL1ixztRNdUt1\nupXXrkclXZyWnFZnytYRGDDt6Bv4Fic9bfoQYi9w8F+7Icxrrx8cp6vPt3YgBGEaMseOcEz0\njDl8dqbx74lEzBKycZAFUIOGCgcNBZ0OhJ3XB85SqZ/NK8hQqUe7Sl8N7+cnFCxKy/i3vh5j\nyFFrTLovBkh03DUTAIB5Ha+t7cbksJ7GiNxZbUdTVx0MBr7k/Athk7+2diAEYQLSY0c4KHld\ne9+hovr3ZiCOrwtZnYLjJqekHayqSVOqtpeWL0i7Vq7TJ8nrTd0XoNm5uu7P57N9iBJK/Mdb\n8f7tHec5Va8GYgNqsnbVF/+lKP2H0zvynxzhSEhiRzgm5Gd8MwmqX4TgblLFoLf9K1cUa3UN\nu0lggCR5fQ2rN2c0dXdF5UfFpZ2fZ7f6Td1lvXHPdndV5lltw1cdF5F2KBhfPzQl68cJabtC\n68lyCsIekMSOcEzIx5cKC291kBocK3jgEaBpq4TUl4npFi81CMAJ0eaMNGIMj2fnq43tJ+sA\neFZVmfoBsuEdYzHgpg1t+0qGx2ll2YdnVKV/xusVnE5u7XAIol22+8JBEGYS3P8os3Q5+PmD\nSIRETnTiOMGdK60dVB81SioZ5SqFplecO3x9CrVaM2eQ6TBfrNWZH5sNKj67qTxlC8YmLCjp\nlOXzL9yQVdvcRMCeg3m28O/7k7e5pmx3y/3tNp7tcwPThF0giycIR0aPTKBHJlg7ij5Ny/NZ\nao2fQPDnsMEf3ijNVKsTpZLRrq73ZmaZ2bKEosOdRBYJ0tbU5h6wcIsUA4gGTmvhZvsoDACy\n3INl7gMDE1+zdjAE0RpJ7AiC6A49xikKpQChWIlLe71B5+sVC65cLdXpKYSeDAl6PaIfALxZ\neCP+YrL5/TzvRIU5atETRiBlocKCnWEI800dbITFKEtPWzsEgjCCDMUSBGGyMp1u2PnLoy6m\nDL+QPOHyFWU7VejWZFyv0LEAwGP8RuGNU3XyJHn907kF5icsrgy9LsDf7GZslO/w/1h2iBNj\nHpPEzgizPhjoNRWWioMgLIgkdgRBmGxzXmFmU/G503Xyd2+UtD2HxfiqSs0ZJCiX5MpD1bUW\nSVjkLLezvNwSLdki70Eboub+JnIb0HeWJvQohBBCRoenMOPk3e1mNbUZFVfev35oStZPk6qv\nbe92OwRhWWQoliCIm+pY7s9amRNFTfVwF1LtZhXpShVuqkFHI5SuNDKLnEEo0kmUo9byTbnd\np6WlmSqLbX56SaFaZam2bI9ryPSAUZvzjy23diAdQvaxdgJjDMAa/RanlRk93sWGb/zzCCAK\nMChKTgKivAauNqM1grAM0mNHEESjDJU6MunCbekZc65cHX7hsow1/l4IALESl+avOYyHGTw0\n9MWAKFem8UVmiItLluWyOgBY4OVhwdZskGf03YGj32Cc/RCycIEeoUs3N0qmKBEjNujisoes\nrkMIzF16jADzADwgVJH8Nqsmg7OE9ZHEjiCIRpvzCmqbkrlrKvX7N9qtAPxKeGisS2MyFy0W\nX1Oq3i0qaVtV7hZ3t/zRo/4aPuTw0EE1er0FQ13h7zPFw92CDdom/7inXENmQLc36GiHs/+Y\n7l3I89p+k3f7j3zWsvFYBUJIKAky+zfb1ADG6tpr6XuiNbXXzG2SIMxDEjuCIBrlarTNqRmF\nIFfT7hauPgLBpfjhKfHDJ7q7Xlerd1dUPZ6TNyM1nTd4n9Sry3m90o2haYQWp18r1esslZ6M\nlEp2Duwr+8JpqlMxWHLdAwIkL/jFhNNbyvllZvnlNy0Yj7VgjPWqcsv2OnJ6RdlFUgCFsDKS\n2BEE0WisqxThxjdyHsNYV6nhd9U8X2nQ60YhcGWYv2VyaJzDBKdk8hOyOgDQK0syv0+88pV/\nynbXT449Nyn5io7H5vc6oab/yTn2WK05U6PsiZNXLLLoCzUGzLNdHRP3iFrm5DWkdQt8u2P0\ndgZztFDa+Wmm0KuMLCQiiN5EFk8QBNHo1fB+mWr1HzUyBHBvgN/aAL/mbz2Zm7+lqITDeKyb\n6/5BA+Qcd1mhELfZ8+rrsgpvAVN89tUj3IBj4RtYxGQzkeandLe6uT0YHPBodm6hVgcAOWrN\nnCvXkuOHxziLzW3a5gUlvqGsOK+10AAfogSYN2FMvDb7WwCgaLFL4IT6ot8Nv0VRQp63750/\nMOabd7+1UIu8NGiyJRskCNORxI4giEZuDP177OB8tfZcvdyDEfAADZP2f6iqfquwuOGcc3X1\ns1LTr6hUGAMCoAEMJ5/vKq/YWV4BgrvBt+GAyTkdQkZmlMl51kcoqNA3dhTxGHSY/7W6Jsa5\nm4sA7IjAJXDQ0lR1dUptzr6K5C3dLkcncosWSkJUlZc4ncmdnZjTamWtdwrxT3xFUfiHvPhY\n9+KxERhbcuqni2+CX9wmCzZIEN2ALDBAYv8yMjJiYmIAICkpKSGB7EBF9F2ZKvWEy1cahlxj\nnMUf9Y98Orfgcr1Cb9UXCgFCbQNYG+D3Sf9IBPBzdU2lTj/Fwz1S7GSV8HrN1W8Ha2qvdvdq\ny9YmQSLXfoGJbwCi835farlmewTFOPFsuxNGLUgSNEkvL2D1MsCYYqReA1cGjHrJ4ouaCaJj\nJLEDIIkd0bdlqdRvFhWX6XRT3N3Pyuu/r6pqflUQUojFwNvqq8QoqQQAztcrAEBAoQODBs73\n9rR2UD1IWZGUeXBsT2wO1pz0IVqEOW3Xs0DfoQ9VXPnQ4vFYmNF+4F4RNPpN0odH9DKS2AGQ\nxI7ow8p1+kH/XmqocoIBhIjS2efeUxSCaCdxRuIIawfSs1h1ZdbPkzU1aZatIYdoQWDCq4yT\nj0fkUmX5OVn+T4zIQ119RZb3gyVvY4N6NOdDlMR/XP+Ff/dU+wRhDJljRxB92uuFN2oMChHb\nelbXfkcSj6FIq5WznCvjyCNfnE6mk10HoMCiNVCA53yGPEQxYkXpqYq0D3ltnYv/aHnBLwgQ\ntv8yxO1BiBK4BOsUhT13B1rk4GW0CRtEEjuC6LuuKlUfGtvm1Xa1n2MgADXPu/1zbr63556Y\n/i60Y6Z38qLfeM6iCzkBAEAaMpNixOrq1OuHpgDmMeD6khMWv4utwZjvyawOAHjvmHt5vUJV\ndVng7C9yi+7JexFEI1LHjiD6rreLShymNwY3ZX2Hqmpeyi+ycjQ9hqIsv0DENXh6v8lfqmuu\nFJ16EPN6jLm20/gQxdAix9/qw8IwlKe8e2V3WNaPE9P39C84fq8D7MJG2D7SY0cQfZeGM3Oj\nTFuEAE7L660dRU8R+8RZvE1MUWm7+mGuo3WjnlF3ACWozvjS4nd3aFhZego37d5RnbHDPWKR\nW7+51o2JcHgksSOIvstLILB2CJaHAYq1lh+stBFir6GUQMLrFRZss77waKfnVGfttuAd+w6M\nseFsSFnuDySxI3oaSewIoi+qZdk7rmb+XuOYG3MVaLQ5ao1DlrVDlDB8yu6co4t6eVDPsVdR\n9ByKYniDrT5qMr508Y7XqUsYkbtr6GwnjxgrxkY4KjLHjiD6HBbjqanpjprVNahmHWU/0zbc\nwhe49puB2uzn1g0ufqO7eCbJ6rqHb7mBGwZc+M8DZRdfvXHmP9e+G1qXf8hagREOjCR2BNHn\nXFGqLsktOZZng2anpic57ky7frdskwRMNL8dZfm5tgcpStj+Fcj8mxINMM+X/LvZ2lEQDogk\ndgTR51TpHLY3q1m1np2UnJ54KdXjn3Ozr1zNVffGjlK9RuASFL3g+JAVhdLgqZZt2dk3IXjs\nFkbk1c73e7bfrv40jHwAXun2rml2BuuUxdaOgXBAZI4dQfQt31ZUrcm4bqWbW3a70k5oeO5C\nfT2P4Wi1bJoqPSdxZK/duncIJSHR8/5I3irmO1zQahJVxb+FFf92fE75XzD7QLvfjVkAu2dY\nKhwHRzPO1g6BcEAksSOIPkTL8/dmZGustpFgb9+Xxw13xblqzYv5RS+GhfRyAD1NJ8+zYFZn\nkgGJMCvIyHGvKMvfi0UUAqBtfFsU03G6Ol19vlAaZu1ACIdCEjuC6ENSFSoV74C167ri9cKi\nP2tlZTrdYBfnJT7ec7083ex/8zGdymobh/SLhRWWr6lnDIKrTj5v+I134dnXSo/56ZW9ctfe\nwOnk6d9Eh0z8xHvQOmvHQjgOktgRRB+yu7zS2iFYjZ7Hp+vkGCBbrfmpqsaLYU7GDR3sYt9j\nYWKvWEQJcMullzbi0CfwUjnseQLOfgffX4UqHgKCYNltsCzs5jmpx+F/x+GaDJzcYOx4WC9p\n3UhNDnx+BE7llVfrvsde7itGhb436dpglx4IFzGArTH3FHM3Tj3kOWAFRTtgdR7CKkhiRxB9\nSIZa1ZcLkhn+2DUcG38xRY9xsEi4K6b/BDdXq4VlBloglQZOlt/4rbduaMIsSZoC0MCnn4Gm\nH6y/C2gZ7DkMb30Mga/BBCEAQOkpeGA/iKLhiUXgx8P50/CUvEULqkxY8yGUe8Oa22CwG5wr\n1u49fG3tZdj2FAy2+HuXVbI6AAwY81q9oojsJEtYCknsCKIPiXVx+aOmztpR2ASMQYN5ACjQ\naKenpBePGeUpsMvXQ9/Yh3oxsbuZ1RVnwc9tN/hgYGw8NCypRQBQDzVT4atpjfUXopRw5xE4\nWwATogEADhwDtRTeeRBGCwAAxsXBp69DlkFj3/0IRUJ45T8wWwIA4DpSmuznnPF59dYL8H5X\nC/DZAUSLha4R1o6CcBx2+UJGEET3PNcv5O86+b+OW+CtezQ8f75eMcPTLje5l4bMYJy8WE1N\nL69NST8J6W2PusDWpsSuwZzEm1W1+vkCANQ1zJGrh5RKEIyEhOZt7SiYMgy2NU8aVEFSAdCx\nMLVpfFZPUV8M1K0DuJgJeLQjldTDrKpc4BJo7TAIB0ESO4LoQ1wZ+t2I8PHJqdYOxOb4Ce11\n21xEMREzf8w+PIMvNAHWAAAgAElEQVRnVb153ymr4MVhbaMBp5YPfQ2GuBsWqzQsVQY5VAN4\nuLYopuprmFrLoBzA0wuayyXHqcpACH4A6TJQAEhNDrlXq+10HeY013+aPOjOq2CJ3UQIgvwZ\nEURf8Ut1rd/pf0lW11ak2GmYpCcm5PcSScB4afAUhHp1kS8tAGdRm/+ELd9UkGn9alybFdtt\na2lj05ttvhTRHWyqYU2aukxNnbWqSxKOhiR2BNEnyFh2WXpmld4Wl09aV4yzODU+zt7H9Twi\nl2LMAaLsptdHAh4AtXIwzOWKqw0eeIA/groaUBteVQOlAC4e0L00HHO6bl3XGyhGbO0QCAdh\nJy8BBEGYJ02pUvKcLQ5EWZuIopxpu38l9Oy/PHTSFy6+CRL/MbTAHlb4usFgd9Bnwfnmzxp6\nOJJscIIYxoXD/7d3n/FRVfkbwH/nTk9mJr33QiBAIAQIvQcjHZFqNDQVXP6sLq6rYll1FXVX\n3RXbCqErIoLoSigKNiwgICDFBBIIkoQA6T2Zcv8vQkuZkDLkzpx5vh9eJLfNb8JleHLOuedQ\nOu2+6VHZPw7TaaIBXds7wE4XOMor5v/adw2rYqwse6/URQAnMMYOwCGEqFVSl2CjzlVXV5rM\nHGQ7z+j7PaPvL/1jV0bqmA54ufO/0YaCpnY40fiB5NaCK9w9gj7ZRk++SfMHkYeR9v9I53RE\nN11zyl306Zv06r+pMJGitZRzhlL2kjqCFrR7YmR9UKJKH3Hl+NvtvVALyNRupuqiWxwk0oXv\nHnKLnCFgkTFoNwQ7AIfgpVDIBWY023SbHSPq7KRJq6y69aHWU2I0/Sc7d2lIYEe+6O1TXXSq\nY14o/QClN7nDi+JbFuxCE+hNI739I72zkdQu1G8w/cePxrx/Y1ydJoJSHqH/7qBPPqGCWtK5\nU58EWjCWwtsZwhnTBo5S68IYY+LtX15PpmxBsCPRbK6pLT2ndu92u+sB7nXEbW370tLSoqOj\niejAgQPx8fFSlwNgfemVVV1++VXqKmyRjNEMb68Po6OkLsQ6yrL3nvkiQeoqbABjZPl/t+Ch\n7xOZ//j+oY6sqDlMkMmde8y9wmRoWYf2QosdgEMIU6t1Mlm5CcPsGjKJ5Kuw0Ycl20Dj1ctm\nFxnrUM22Wfzx/UKbmvpEJncOTfgAqQ6swu6HlQBASygFtiE6SsakfPpTLdjoB87ynNwnzp43\ncdF9UVXwG1JdY4Kiwdg12/q79u291CV0otRVACds9HMWAKxukqf7fT7eEia7arNZuhdvjlEU\nX/0j+2+ZWVIXYgVKbbDUJdgis6FDZ29utY6dgxD4hmAH4ECya2o6pqWCkf2t+LT+8hWpS7AC\nhcaHSdouC60mV7uGT5G6COAHgh2Ao7hQU/NVUXHHvJZoa31dLWCw7UeGWyg/bRUeiSOiDl6H\noz38+/xdpY+QugrgB4IdgKMoN9loT6iN6KPT3vogm1dblmWNy9h9m58oNlqezFapdKFSlwBc\nQbADcBRRGrW7Ag/CW7QyKlLqEqxA6zuozecqdVfH58lVrozhVukQTKYLGCV1EcAVBDsAR5Fd\nU8tFZ+NtISMK0/Aw2YRr+N0+vR5ngpyIBJm6VefWlv1BRILCKXDIcrV799tSH9SnUHvKNV5S\nVwFcQbADcAiZVdUxB38tNhpvfWjLqAW77627mcAYN6E3oP8rsfeXh9/5qdlU3YbTzYbKrD3J\nxqo8qxcGjcnUHlKXALxBsANwCKsuXiq36mwj1Xy1/hlE8UJ1W2KQbWIyVeWlA+24gGioRLDr\nCL69l0pdAvAGwQ6Af3m1tTsLi/CsZPPmpmeWGO1mxH3zynK+uXTsDamrgFvDvMRgdQh2AJyr\nNJnjf/3taHmF1IXYum+KiqefbHpde7uTu/9xO3os1MZ03BgDJigrLx3ASiFgXQh2AJz7vqTk\nQnWN1FXYAZHoy6KiKSfTpC7ECmrKzpOI2W3apuNatkVz7ZkvRp/a3NNYU9hhLwrcQ7AD4FyZ\nyfotN4zf0LDtSgEHrZs6/2GE9SfsRE1R2uUj/5K6CuAHgh0A5xLd3AQr/x8vioznj44LNXbf\nwBk4eLmzzwCpq4AWYUyoLjkjdRXAD54/nQEgp6b2sbNZnnLrTjbLeVPQwvTM74tLpa6iXRRO\nvp3v+jFy7HYnj54k42F+Ptvh2/spr5iHrXhBUTQ5+/Sz4gXBwWFucQBu1ZjNCcdOpldW4nHY\nVrloMEw58XvOwL4qwb5/9dWHjNOHjCOi3z+Jrco/JnU5toa1bThdRd5+xlibT2/MLWKadw9r\nJkVwcAh2ANw6Ul6RVlkpdRX2RxTFAqMxrbKqp9ZZ6lraTTSf3T0Fqa4JTGxbMCvL2WvFKrpM\nP+rk0dOKFwSw799HAaAZBsxc11YCo0AVDz2YpRd2F5/7XOoqbJIN/ONQuUQi1YHVIdgBcGvl\nxUtSl2Cvng0J9lDw0KGRf2qV1CWARWZjldQlAIcQ7AD4ZBTFjy/nS12FXRKIHgsKkLoKKyi/\nuK/43FapqwDL7HwQJ9gm3FUA3DKhK7ZNRKIaLibqK0hfJ3UJ0BzPqDlSlwAc4qGvAQAam3Ey\nHcGuDRixUW4ublaeIEYCNaWZBWlrpK4CmsaY4N452S/+BakLAQ7Z/YcXADRpewEWKWo1X6Xi\nLk+Pf4SFSF2IFRSd/hCritkOQaZhchUxmVfXBV4x/ydTuQkytdRFAZ8Q7AD4ZERzXeut7RKV\n6O4qdRXWIQoyqUuAqxiTR4zbrgsYKXUh4BAQ7AA4lFtTi7aa1op2duIm1RGRV/SCvIPPi2aD\n1IU4MubX93kmyF3DJqvdoqUuBhwFHp4A4FC1Gbmu1fJqaqUuwZrkGs/Od/0kU7rUfctkKsb7\nWnC2R5QpnH3jnkSqg46EYAfAoTCNOliNETxERIwxRcs+6JQCb7mnpjTTVFtS97VoqmnjSgvQ\nDsaqK1KXAA4HwQ6AQ6VG0+XaGqmrsAmiKKpkLUpsehlvg9KqC09KXYKjkGu8G/x/yoiImD74\nTmkKAgeGYAfAocFHfqs221rzTBX9bTCNeallB5fTo4Np/CsW96c+SiMS6JeWXctkakmyKzIa\nW1ab3dB49JC6BCthNt2YKsg13jF/limurizMBBmTKRXaoJDhK7X+w6StDRwQHp4A4M356poT\nFZUtOnTLQ/TOcZLF0sdvk0ejvRkr6IH1RFp6bRf1tnaVt6CggZMowmq5pCUht4dWa62XsxGu\nEXd7dJlXkLZa6kLazbYf8TYbqy8efDZywpeGyjxB7uwSPIbJeFhoGOwUWuwAeNPKGewUxI7S\n7uxG2820cxdJNk+viu5+jP6U2GGv5y5XLI8M67CX6ygsZMSqbkmZhMcmbi9RFM2V+UfdOyW5\nhk1GqgNpIdgB8GZz65aIjaQYDe3c3nCz8SDtuUxdu1mvLts1xEWf2T+um7OT1IXcFip9uFun\nmc0fo3QOlGs8kP/aQ6HxkboEACIEOwD+/NG6aTsYDRpM2Tvpt/ozpPyQSqWh1M/32vcF9PAw\nuvNJatDH++E8GjGODl2bLC1jJz09nyaOotEJlPQgrdhDFh/hEOnEVnp8Dk0YRaMTaOY8Wr6N\nyq7vbTDGroy2LqN7xtLoUTTrQfrwsHWf79xXUvrqHznWvKKNCUvYGDpynT5krC4gQa5u3OlO\ntRXZxqqClnVZ10EErEfjHuMafrfUVQAQYYwdAH/CNKrz1dUt/i/aQL3vIN1XlPoz9Rh0bWMZ\n7dpHneZS4NlrWzxowkD67SfaW0gT3K9tzKavT5P3dOqtICJKW02PrCbfBFo0h9zMdGIXbXqO\n0orp9alNxIAjy+mxT8hvOD00mzwY/f4lffg6HbtM/11AikYHf/EMvX2Ies6ghX3JnEfbX6Ei\nq352MUotKHo5nIeVxCxx75zs3jmZiI6ucmn/1WqYWiVWtf86HGCC3Cf2b759nsESYWAj0GIH\nwJunggNb8w9bJEU/SvCi77bfaI0r+JIOmmjMmHqD1odNJr2Jduy6seX8V3SWaMx4YkSUT++t\nI8UQ+s9zlDiE4ofRvGV0fwwdSaH9jVvtLtL7W0g1kN56kcaOoH7Dac4yeiiWzm6ivSWNDj5D\nmw6R6zj652Ia2p+GT6Z//pPECzcfIWPMQ9H2qMeIvJSN4ySfnNy7E2v3xC62/TRDxxAEeeCQ\nt7rdk+Hf7yWkOrAdCHYAvBnl5rqrZ/fWdJUJNHYs1fxEe689dbErlWTxlOBV7yhFX0r0o7RU\nOndty969xLrSmHAiosqDdNxEcaPoxqJcjEaMICqnQ2kNX7DsEKWL1Ovmg4kGDyEy0JETDQ8u\nPkG5RLH9SXm93jAaXK91zSSKBYa2T1YiEFsaHNDm0+1L0ND3FE7tHQ2mIK5W6Wgbt6j7vLv/\nn1LHc0Mv2CMEOwAOJbi5DHbRt+KEyHHUyUw7dhIRUSbtOk2DxpGuwUECjR9PdJ52Hiciogz6\nJot6j6e6kHDlEolE3z9HIwbf+DN9ORHR5UsNXy7/ChGRj2+9jV7eN3bdrLCAiMij/sgwT89W\nvLtmucplv/WJHeXGzyqxzdN49Iia+F07B8kJ5OBr1jEmU/r1eU7qMgCagDF2ALwpMBj3lZQs\nCfI/VFpRJZpadpI/jetF/9lOZ5OoMpWyXejPg5s4Kng89VhNX+2gBTGU8RVlq2nuqKu76qaQ\njX+A7u7S8CxXC9OIGBqsTy/euM4tmVr4vm7NX6XsyunzsJYYqi625jkJaEwUTbVVBUeVumCp\nKwFoCMEOgCv7S8vuPHaypA25Z9R4eu8F+vp3KttDXndQnybHnHnQxEH04rd0eAkd+pp0w2nI\n1dn2ycuXGJHoTvH9bv1a3j7EGrXk5eUREXl7NzzYxZWIqKCg3saLF1vwllqkm5PzrQ/ii6Hs\nD6lL4IFc0+heBbAB6IoF4MpjmVnlbWvN0g6nIVr6OYV+LKTEsRZ76oZOIn0Z7V1F31yk0eNv\nPMGq6UM9ZHRkB128qSnol9dozmN0plE9zn2pq0CHv6Si65vM9O23RGrqG9PwYI+u5EV0dP+N\nmVNqfqMfGnXvtolA7OmQQKtcyo5Ul5yWuoSOZvXZWVxCxjn7tOB3GIAOhxY7AK6cqapqJtYx\nYqLFPjgljR1NS7YRi6IxnSxeou4Rii0bSQyksbE37fCgBffRI2vp4UcpaSz5qunsPvowlXxn\nU1jjZzC9acFMWrKRHn6WkkaTay0d3UGbT1H3P9HQxut6RdOUrvR+Kj0qp3G9yJBDqVvJJYIu\nWmXmOVFm2+uQ3g5q10bd5byzVseza/hkJ88+Ktco1/ApmMwPbBOCHQBXBur12/ILmtwlMHaL\nSSpiJ5D/NvIaR/7NHCTQhAn0yQrqNI4i6u+Jvp/e9qN122jVy1QlkGcQjfkbzZ7Y9MdMzEP0\nHz9a9xm99SzVyMg7nGb8nZJHUxMTcTCa8QoZ3qTte+mNneQRTuOfpdCd9EwWGRof3Dpmoh9L\nSnldc8IS18jpLhmbSrL+J3Uh9uH6r0NqfUTw0BVyjdctTwGQEBMxHRFRWlpadHQ0ER04cCA+\nPl7qcgDaLrumNvqXX2/ujVUyYYirfqyb66Nns6zzGqf/Sws+pr9upXHutz7Y5u3o0XWMu5vU\nVUigMv9Ixo4xxgrrdGpzTOs31K/PM0RM6z+ECcpbnwAgKYyxA+BKoEq5uku9jlSVIKzpEumn\nUlmn30i8RCu3kfsdlMBDqkt0d0t0c8RUR0ROnr18Y5+QugrbxgQi5hv3hC4wQRc4CqkO7AK6\nYgG4YhZpbV69Nphyk/EfWRfW5F1ub+P8peP0+3nas4EOyenpB0jVzstJKdpJM8fXO0brnOjm\nJjjwQCmvmMU1JWfyT60Uze3u1eaOXOOtCxjp0WWuPugOqWsBaAUEOwCu7Cgs3FFQVG8TY1/k\nFxrbP+ji1FZ6+Ttyi6JHX6RRVpsfWBKnK6uLTaaRri6OnOqIiDFZ0JB3Age9mf3D4vyT71t+\nsMYRdZq4V+PeXeoqAFoNwQ6AK+erGy7MKopiXsOpgNtkxHM0wgqXsQUmEl8+n11jNr8eYWHy\nZEfCBLlH9Pwrp1ZgBVgiIiaQaNYH3oFUB3YKY+wAuDJAr3O86TvaaPPlfKlLsBVOXn2cHDXH\nCDKVILs6sEDjGesSOsE//sXwO7dKWxVAm6HFDoArcTrt25HhD2ecs0LfK9cYkU7WxNwqDkuh\nC6aC36SuQgpM1mPOparCE0rnQIXW4WarBv4g2AHw5k8Bfu4KxaxT6VIXYsuYSOJfggKkLsMm\nFGVsuvjLs4aqS0SMzzVkmUCiuek9xHQBIwSF1tmnfwcXBXCboCsWgEPFRqPUJdg0xsTPukc/\n4OcjdSHSq7xyOGtPUnVppqm2lBjJNJ7EXV++X9xSre/AJndpvOKCh73fwfUA3FYIdgAcitJo\npC7BpgnE7nB3lboKm1Ca/ZUomq82aIkiGWu4WylLKDm/I3zsFzr/es/+CEp95ISvukw9pHBG\nwy1wBcEOgEMj3VxGuSG4NEHGiIiSfbw0Aj79iIjkKo8b3zBBULla6rW0W+bK/F9FsyHsjk0K\njff1rUGD3tQHJkhYFsBtgjF2AByqNpsrTOiNbYwNddFP8nR/yN9P6kpshVunmZeOvlpTkklE\nJIr+8S9k//CIqbZE6rqsiQkymUIvyDXRM47nn1phrClyDZ2o9R8mdV0AtwWCHQCH3s/N219a\nLnUVNoeR2EPr/HCgv9SF2BCZQtdl2pHC9PXG6nx98Bhn7/jKS/uvnORq2JlnlwcEuYaI5Bpv\n395PS10OwO2FYAfAmwqT6Z2ci1JXYYvEa12xcDOZQufVfdH1b/37vXzl1CoS+Wnx1Xj3lroE\ngI6DUSYAvPl71oUzVdVSV2Gj7vPxvvVBjk2mcoua9DWT2ehiwKz5ZzuYoHLt3GBbXXMdgINA\nsAPgzTfFJWiWaowRBalUsVpnqQuxA1q/IaEj1za5S+Pes2Nraaj5BW3lKveIMZ8rnK6PoWRK\nbaBL8LgOKAzARqArFoA3gSrlsXIy8TjRbLsw9q+IUKmLsBtukTOri09fPva62VQtkzsba0sY\nk7uEjKkpOiN1aTcwJhdF0/VJlRkTVK6d1K6dY5KzC9LXluV8o9QGecX8WabCE+LgQJiIdYeI\n0tLSoqOjiejAgQPx8fFSlwPQLr+WlQ89cqLCbJK6EIkpGTMQXf+I2xjdeZaPp7Ql2SNRNDF2\nY+21oylas6Hipv1SLlbhHnUfI1aYuUk01RIRE5SR41J1mMQEHBu6YgF4E6fTnu4X90Swo696\nWSuK8mvjsRb6+yLVtc3NqY6IBJn65m/Vbl0CB77OBGXHFnWVLnBUyKh1PecWhoxYEzR4eddZ\np5DqANAVC8Ahf5Vysqf7K39kS12IxHb37FpkMEZo1D0xtM5KvLotunj4hWvfsaDBy3WBCSJR\nzk+PdmgdjHl2XeARdR8RCQpnjy5zOvTVAWwYgh0An6aeSJO6BAkIRMSYWRQFRiEq1VAXvYy7\nlU+l5Rf/nNzJM//3VTKZs1/883UtZPrAhFxBJprNVuiWZYyaHSDkGj4leOh7TK6RKXTtfS0A\nHtlXV2zZsbUL49wExmJfyWhqf8351Jfnjuoe6KZRaVz9u41IXpaaVdvRRQLYhJxah7v3BUax\nOu1Avc5FJhvp6rqjRzekutuAeXVfHD3taNSUH6/3e2o8eoSMXK/UBjBB6RI6odOEPb5xS+Xa\nNg0GEEWZQtvMfvdOSXKNN1IdgCV202JnyP5y2YMPvLQzX6Gx8MucOfP9Sf0X7i70jJ00Y3Fn\nl4rMvVs2PjV+5w8rf95+f6R9BVgAaAOzSLWiebGf32xfbwS6Dube6R73Tvdc/1YXOMo//h/n\nv51fkLbW0imMWOO5SxiTOfsONFTmVhWcqDuGKXXm2lJiAolmlT5CH3TH7XkHAJywl2D39aIe\niauVw/72v9TAd2IW7W7iiCsfLvnr7nz/mZsPfzjNVyAiMj+zdVavqZuXLNk0+X/3YNg0OBg3\nubzQyM/iAS10orxybtqZQoNxSRDWDZMaE0JGrPHv/0pV/tGc/U9UF/wmiuab9/v0fvLSr6+K\nYr3Ht0USlfrwyPG7K6/8Wlt+3tmrr8LZv+D0hvLcb5XaEK+YxUKz7XkAYC8tWQbfxNf3nfh6\n2YRQCw9fFWxdl1pOcY8su5rqiEjwvfvFP8dRWerqLZc6rFAAyVWYTE+cPV/keKnuulUX86Qu\nAa5SaHz0QYkuQXc2GDnn3jnZP/7FbkmZ/v2Wefd8VKbU122Xq9x9ez1ORE5eca5hdym0gcQE\nj86zQ0as8ev7nFztIcF7ALAr9tJil/jCR4nN7RcP/LTfRMHDh4fV29xp5MhA+vXnH/eLCyeh\nawYcxIOnMzdeuiJ1FVKqrN8yBJLz7vVYUdbnNUW/E5Fc7RWa8EFdj6pSF+Ib9yQR+cY9WZL1\nOZHgGjZJpnKTuFwAe2Yvwe5W8jIzK4j6hIU12B4WFkaUnZGRSxRQf09aWlpFxdVpNrOysjqi\nSIDbzyCKW64USF2FxHo6o7fOtshV7l2nHyvP/U4UTVq/oY0Xb5WrPTy6zJOkNgDO8BLsysrK\niEirbfhxrtfriai0tLRRsJszZ86BAwc6qDyAjiIQCQ7fOv1YUMCtD4KOxQQFZg8G6AD2Msau\nZVijuQ3qlhNqvB2AUzLGHvTzkbqK24jRLf41K5kwyAVzYQCAg7KxYFecksBulpBS3LITXVxc\n6GrLXD0lJSXX99a3du3aQ9ds2bKlvZUD2IzXI8Le6hTO6y8zjN1iCtwJnljxHQAcl411xSoj\nE5KSfG983z2yhSsQ+nTqpKcfMjMzibxu3p6RkUGki4rybXRGly5drn/t7IzlhoAfcsbu9fFa\nnHFWusXZbyOzhTfFiIiRjNgTwUEdWhAAgC2xsWDnNPyJD4a36cz+Q4co16Tu3ZtO/Tvf2Hps\n95eXSDF++EBeWy8AmrT5cj6Xqa4ZvkrlcFeXRQG+fXR4cgIAHJeNdcW2nX7KA9M96MTyJzdk\nX5vt0pi1Zum7v5PXjIVT8PA8OJAyk+kvmeekruK2aOY3NE+FYmPXqEEu+o6rBgDA9thYi50F\nFT+999TmdCIiMpxMI6Kc7S89kudCRCSLm/t6ck8i0k969Z3p387aPCe297Zpd3TRlaXt2vzZ\n8eLg5K3/GoeB1OBITlRUVpo4nciNWVxl/vfKyqzqmlC1qmMLAgCwLfYR7Kp+2/rmm3tv2pD/\n49o3fyQiItmMwXXBjsh/xoe/ePV7dtnqHRvf2l6r9o7q9+Dyp59fNJjnJwQBGgtQtnBoqv0R\nRRKImZsKd0ZR/L64JNTXu+OrAgCwHfYR7DwX7hEXtuA4ud/IJStHLrnt9QDYsmC1aoaX58dX\n8qUu5Lbo7KT+o6am0nTtIYqb2vC8lQrJygIAsA3cjLEDgBvGeHA7rjRMrf5rYICXUqmXy7s7\nO11PdYNd9QlumOgEABydfbTYAUCrZFXXSF3CbcGILhlqnz9/QWCMSEyvMq3q0uliTW2oWjXd\n21OOmcgBwOEh2AFwqL+ezyeGGGOFBiNjZBZFIhJJzKisWhYeInVdAAC2Al2xABxKdHf1UPD2\na1sfne6HXjFamfx696sokgytdAAAN0GwA+CTTsZVsJMztj0meoBe96C/j0gkEBMYqWXCPT5e\ntz4ZAMBhcPXRDwDXddFosqqrpa7Caswk1opmIloU4Ocml3+aX+Aiky0O9I920khdGgCADUGw\nA+BTd63TrqIiqauwGrNIj2Sc3dotmhEl+XgloaEOAKAp6IoF4I1JFP+bm7ctv0DqQqzs0yuF\nmy7zOTkfAIC1INgB8GbpufMPnc48W8VPP2wdgdHqi5ekrgIAwKYh2AHwZkVuHllcUtWeiYzD\nNwUAYFUIdgBcEYlMIof5R2BkJjHZF0PrAACag2AHwBVGdJ+vDxEx4mSCN8aYq1x2p7vbxq5R\n9/l4S10OAIBNw1OxALx5IyLUT6lYn3flTFWV1LVYhbiyc6epXh5SlwEAYAfQYgfAG5UgPB0S\nNNbdRepCrGM1Uh0AQIsh2AHwaU9xqdQlWMcf1TVSlwAAYDcQ7AD4xE0e+r6kROoSAADsBoId\nAJ88FZyMoC03maUuAQDAbiDYAfApiZcHSEe4cjJYEACgAyDYAXDIJIpr8i4Jdj7hiUCU5OP1\nbGiQ1IUAANgNTjprAOBmZ6urc2pqpa6iXZyZrGhoPwWz83AKANCx0GIHwCEfhVKw80g0L8Ab\nqQ4AoLUQ7AA4pJfLngkJlLqKtnCRy7s5Oz8TEvSv8FCpawEAsD/oigXg03OhwbFa52kn0412\ntXTspuioOz3cpK4CAMBeocUOgFsGUbSvVPdaRBhSHQBAeyDYAXDLX6mUuoRW6Obs/JdAf6mr\nAACwbwh2ANwa6KKf6OkudRUtdbKi4slzWVJXAQBg3xDsALjFiD7rHv2/mOi+eq3UtbTIuzl5\nZnvqOgYAsDkIdgA8Y0QTPNx/ievppVBIXcutGUTRTEh2AABth2AH4BB+iusRr9fKGDnJBJnN\nzA8nY8yZCQK7WtAMb0+5zdQGAGCPMN0JgEOI1KgPxPWs+zqnpjbo54PStoypmDDa3eXfkWGl\nRtML5y/k1NQmuLk+bZ9z7wEA2A4EOwCHE6BSPhoU8NqFHAlrqBHNs7y9IjUaIvqse7SElQAA\n8ARdsQCO6F8RoZ93j+6n13krJRt7l15VJdVLAwDwCsEOwEFN9HTfH9djf6+eHTCorcmX6KOz\nj2d1AQDsCIIdgENLr6q8fYPtEt1dd/Xo9nG3zp4KBRHJBeYlVxCRwOjhQP8JHnYzxx4AgL3A\nGDsAh5ZVXWPdCyqZUCuaZcQeCw54OTykbuNED/czVdWBKqVeJsuoqvZUKDwU+PABALA+fLYC\nOLT+eh1jRKJ1po/zVMh/7dPLLIpucrleLru+XS0IMc5OdV93dtJY46UAAKAJ6IoFcGixWue3\nIsNVwo0Qxm9JNQsAAA7jSURBVIgJTQ+Ku7rX0q43IsLO9u8TpFKGqFU3pzoAAOgwCHYAjm5R\ngF/ZkH7LO4Vr5TIiClMr9/fuMdDFhYhkjIgoVus82EWvk8sGu+iP94093if2i+7RB3v37KXT\nsmvHDHDRPxzor5MhzwEASImJIhbwobS0tOjoaCI6cOBAfHy81OUASMMoilcMBj+lkohKjaY3\nsnOOlFfEap2XBAa4NNUCV2gwvnoh+3h5ZazW+bHgADc5hnYAAEgMH8QAcJWcsbpUR0R6uey5\n0ODmj3dXyF8ND73tZQEAQIuhKxYAAACAEwh2AAAAAJxAsAMAAADgBIIdAAAAACcQ7AAAAAA4\ngWAHAAAAwAkEOwAAAABOINgBAAAAcALBDgAAAIATCHYAAAAAnECwAwAAAOAEgh0AAAAAJxDs\nAAAAADiBYAcAAADACQQ7AAAAAE4g2AEAAABwAsEOAAAAgBMIdgAAAACcQLADAAAA4ASCHQAA\nAAAnEOwAAAAAOIFgBwAAAMAJBDsAAAAATiDYAQAAAHACwQ4AAACAEwh2AAAAAJyQS12ATaip\nqan7Ii0tTSaTSVsMAABAmzHG4uLipK4CJINgR0SUk5NT98Xs2bOlrQQAAKA9ZDKZ0WiUugqQ\nDLpiAQAAADjBRFGUugbpVVZWHjx4kIh8fHyUSqXU5VjH1KlTjxw5kpSU9MILL0hdiz2ZMGHC\nqVOn5s+fv3TpUqlrsSejR48+e/bsokWLlixZInUt9mTIkCG5ublLlixZtGiR1LXYk759+xYW\nFj755JP333+/1LXYovDwcKlLAMmgK5aIyMnJadiwYVJXYWVqtZqI9Ho9/oW3Sl2yd3Fxwc+t\nVRQKBRG5urri59YqcrmciNzc3PBza5W6wdAeHh74uQE0gK5YAAAAAE4g2AEAAABwAl2x3Orc\nuXNtbW1wcLDUhdiZ6OhomUwWGBgodSF2plu3blqt1t/fX+pC7ExMTIyXl5efn5/UhdiZnj17\nFhUV+fj4SF0IgM3BwxMAAAAAnEBXLAAAAAAnEOwAAAAAOIFgBwAAAMAJBDsAAAAATiDYcabs\n2NqFcW4CY7GvZDS1v+Z86stzR3UPdNOoNK7+3UYkL0vNqu3oIm3bzvnOrCnaObukLs0W4Y5q\nA9xjrYDPNIDWwXQn/DBkf7nswQde2pmv0Fh40tmc+f6k/gt3F3rGTpqxuLNLRebeLRufGr/z\nh5U/b78/Ehm/jqG4uJIoaOSCKTHqejtU8aHSVGTDcEe1Ce6xFsJnGkBbiMCJvQ+4kcxn2JP/\nO/5OIhH1fPlMwyMur5+oJfKfufmi6eoW08Ut032JdBM+vNLB1dquy++OJKJJG6qkLsQO4I5q\nG9xjLYPPNIC2wK803DD4Jr6+78TXyyaEKps+oGDrutRyintk2TTfa3/tgu/dL/45jspSV2+5\n1GGF2riioiIitZub+taHOjrcUW2Ee6xl8JkG0BYIdtxIfOGjJQM8Lf+Figd+2m+i4OHDw+pt\n7jRyZCCZf/5xPyaqrlNcXEzk6uoqdR22D3dUW+Eeaxl8pgG0BYKdw8jLzKwgCgsLa7A9LCyM\nqDIjI1eSqmxPUVERkXP5kbcWJPaK8NGpNK4B3UfOxXjsxnBHtRXuMevAHQjQFAQ7h1FWVkZE\nWq22wXa9Xk9EpaWlEtRkg8Ti4lKizJQ/P/OdodPIpIcWzhrhc+X7tU+Nj0t846RB6upsCu6o\nNsI9ZiW4AwGagqdiHQxjrMEWURSb3O6gDGGJf3k0RBU5+ZEFg73qfiZi8c9PjBz+z2+fXLxy\n+td/CpS4QFuDO6rVcI9ZFe5AgPrQYmdvilMS6s18lZBS3LITXVxcqKnfYktKSq7vdShN/ySV\n8fe/9tprLy289j8uETHXAS+9mKSj2u927KmQsGBbgzuqjXCPWQnuQICmoMXO3igjE5KSfG98\n3z3SwgNjDfl06qSnHzIzM4m8bt6ekZFBpIuK8rV0Iq9a85OUBwf7E6UXFZUSOXdAbXYBd5RV\n4R5rNdyBAE1BsLM3TsOf+GB4m87sP3SIck3q3r3p1L/zja3Hdn95iRTjhw90uG6LJn+S1Uc3\nLNvwk2nAUy9Nvbk/rPLkySwiTXCwZ4eVZwdwR7UJ7jGrwR0I0AR0xToO/ZQHpnvQieVPbsg2\nXd1kzFqz9N3fyWvGwiluktZmM9S6C9v//d9ljzy+Jef6XAnmK3v++tynNeQ6eVqCQsribA3u\nqDbBPWY1uAMBmsDqhpmCvav46b2nNqcTEZHh5Gfv7jnvOWhOUh8XIiJZ3NzXk3sSEVHuxzP6\nzdqc695z0rQ7uujK0nZt/ux4cVDy1gPrJqPX4qqy/c+NTHj+UKVLtzFTRnf3Ml069e0XO44X\nysLv2/jD+ql+UpdnY3BHtQXusZbAZxpAG0m78AVYy5X3Rln6K5bN+OTGcYbcva/fP6qbv16t\nULsG9EhcsHxfnsnyZR1Tyalty+aO6hnhp1cp1Hq/6CEznlh3uEjqqmwU7qg2wT12S/hMA2gb\ntNgBAAAAcAJj7AAAAAA4gWAHAAAAwAkEOwAAAABOINgBAAAAcALBDgAAAIATCHYAAAAAnECw\nAwAAAOAEgh0AAAAAJxDsAAAAADiBYAcAAADACQQ7AN5lvBLLGGOD386zdET+28MZYyz2xbSr\nG6o/GM8YY4zJh76ba/nKJR9O0NQdN/6D6qYPyX1nhJwxxliXp440fcT116pHUDq7BXQZOHnR\nazsyKlr4Ri0oPvDvScEKxlj36+8PAIBXCHYAYJlp37oPz1raeeWTDbss5Llrzqxe+Z1JUKuV\nlL42ZZ+pmSOVvtG9b+jVLVBrvJz+8+fvPjauR+8Fn19sY/1lR9+Z1mfokv9dMLbxAgAA9gXB\nDgAs8IqI0NMv69b/3vTuvE0b9hh1oaEeFi9g/mXlqmMiG/D40wlKyt2YsqPK8ov5zV5/6IZf\nj5+9VFaSmfrCnYGsKn3FPcmrmmk4tKDi5JqkvgP+b1tN4hvPTlC1+nQAAHuEYAcAFpgHJd7p\nTCfXrz8sNrH3/MYN+0zKkcMHmi2db/hq5bosYoOmzV80804VFX+SsqWkNa/vFD72mU/WzAsk\nqtzz9pr0VlZv+uzpeR8V9H3my0Of/6WvvpUnAwDYKQQ7ALDAIE+46041nduwbl/j8Ja2Yf1B\nUZ4wKUFeY+H0ss9WbLpM8uH3zQpyveu+8c5UtSNlY2v7VLWjJo5wJqJjhw+3tjdVF/vQp4f2\nvjDSh7XyRAAA+4VgBwAWGAyqcdPu1FDOR+v2GhrsO7Hhg2OkTJg+WWdouOuqSx+t/KKcnMbP\nn+lNpJ04b7onGb9PWXu6lUUwHx8vIhIrKprpx22KbOLf350comjlywEA2DcEOwCwwGw26yYl\n3+VC+Z+sS628eY/4y/oP08lp3H13u5nNTXfFZq1ducdALlPnTdETESkT598bQPTrqlVHmurX\ntcx47lw2ETn7+ena+DYAABwIgh0ANEM9bvZ0Dyrbtm5b2Y2N5u83bDxPuruS79JaOE08ujLl\nkEh+98wfo6nbIhs0f04Xosx1Kd+2pk/1/Mr3dxqJ1KNHD27rWwAAcCAIdgDQHGXC7FkBVJm6\n7pP8a5sMX2/YlEMeU5PHaSycZPxmxZoMok6z5w+VX9/Yfd68eEaXNqZ8cetOVdFQmnvml60v\nTk549NtKUsb89ZkZru1+KwAA/EOwA4BmCYNm3xtJhr3rPsqp21C9c/2WfPKfmZxgaQBb5fYV\nGy8S9Zg7r/fNm8OT5w9XUPGnKVuKGp9z/tW+9eYndgmI6jf1mc8zqnQ9F3y8/fk4eeNTAACg\nIXxYAvBOEAQiIkuD4YjIZDIRESkUTSe1PrOTu7767L51G84tfiKMKj7fsK2EQh5MHmbp98L8\nj1duKyGi35ZGsaWNdxt2p3yQfd/iwPpbVYE9+4Tc6NllcrXOM7jrgMSZs+/u44kPKgCAlsHn\nJQDvvL29iYgKCwuJ/Js8Ii8vj4jI37/p3RSdPLvv3x8/uH7DySeeDdy24Yty6vxwcrylWUQu\nrF+5q5aUgb0GRDSePq7y/MGDWd+nrDm9+Jmoejt8k1J+eKVPS98TAAA0CcEOgHfaqCg/2n3x\nzM8/51N3zyYOuPDNN5lEFBETY2nMXMi9s4ctPfjtp9vSFgdu2l1DvZKTu1t6uROrVv5sJu2k\nN3/YPMWp0V7jnvv9R6/6bfWqg0+/2hcTzAEAWBnG2AFwb0BSUjiRec8br/zSxGML+Z8tfe2A\nSCx27pxYi5fwnzU7QUnHdmxZ9cVeI+uXfG+UhQPN+1auTiNyvevecY1THRHJR9473Z8oa13K\nHizfCgBgdQh2ANxj/R57455AZk57fdwdj246cuXajMLm8rN7/j1ryKwPckjR7W/vPBLZzDXc\np84e70wH//PK3mr58OR7gi0cVr1zxYYLRB7T7h1jYXVWYVjSzCCiSx+lfFHRjvcEAABNQVcs\ngAPwnrQy9X3jtIc3//DGrLh/O3mHBntpTMU553JKjETMve+fV21+aaBzs5fQTp49Rf/phoIS\n5bjkmd4WDiresnJLEZHv9HtHWVzygQ24d1bEG//M/Cxlc/5dc5vqGraKzBVTZ6zIuvpNybla\nIsp8b2qfz9R1W0If/HjLgxG368UBAKSCYAfgEJx6PPDx0VHz1qZs2LJjf9qFP87kyl19I/sN\nGT5p5ty5M+J9b/1RoBkze7rXhpSKccl3u1s45OKGlanVRCGz7h0ia+ZKvZLu6frPf5zanbIh\ne+5fAps5sD2qck8cPpxeb1N17snDuVe/Ls9t5QplAAB2gYli69b3AQAAAADbhDF2AAAAAJxA\nsAMAAADgBIIdANiH6g8ms5bRztkudbEAANLAwxMAYB/k3e9+/PEuLTlS1bu5mVsAADiGhycA\nAAAAOIGuWAAAAABOINgBAAAAcALBDgAAAIATCHYAAAAAnECwAwAAAOAEgh0AAAAAJxDsAAAA\nADiBYAcAAADACQQ7AAAAAE4g2AEAAABwAsEOAAAAgBMIdgAAAACcQLADAAAA4ASCHQAAAAAn\nEOwAAAAAOIFgBwAAAMAJBDsAAAAATiDYAQAAAHACwQ4AAACAEwh2AAAAAJz4f33UoCX6JKW7\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DimPlot(obj.atac, reduction = \"umap\", pt.size = 0.5, group.by = \"cell_type\", label = TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6b66bda3-1fb2-4160-bb75-759accb53f7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Creating h5Seurat file for version 3.1.5.9900\n",
      "\n",
      "Adding counts for RNA\n",
      "\n",
      "Adding data for RNA\n",
      "\n",
      "No variable features found for RNA\n",
      "\n",
      "No feature-level metadata found for RNA\n",
      "\n",
      "Adding cell embeddings for umap\n",
      "\n",
      "No loadings for umap\n",
      "\n",
      "No projected loadings for umap\n",
      "\n",
      "No standard deviations for umap\n",
      "\n",
      "No JackStraw data for umap\n",
      "\n"
     ]
    }
   ],
   "source": [
    "SaveH5Seurat(obj.atac, filename = \"snATAC-seq.h5Seurat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f6abfc39-1b21-4f08-a081-076d16af26a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Validating h5Seurat file\n",
      "\n",
      "Adding data from RNA as X\n",
      "\n",
      "Adding counts from RNA as raw\n",
      "\n",
      "Transfering meta.data to obs\n",
      "\n",
      "Adding dimensional reduction information for umap\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Convert(\"snATAC-seq.h5Seurat\", dest = \"h5ad\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.0.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
